development of rainfall runoff model using …...priyadarshini watershed”, submitted to faculty of...

DEVELOPMENT OF RAINFALL – RUNOFF MODEL USING

ARTIFICIAL NEURAL NETWORK FOR A PRIYADARSHINI

WATERSHED

A Thesis submitted to the

Dr. BALASAHEB SAWANT KONKAN KRISHI VIDYAPEETH

DAPOLI - 415 712

Maharashtra State (India)

In the partial fulfillment of the requirements for the degree

of

MASTER OF TECHNOLOGY

(AGRICULTURAL ENGINEERING) in

SOIL AND WATER CONSERVATION ENGINEERING

by

KOTHE SWAPNIL AJAY

B. Tech (Agril. Engg.)

DEPARTMENT OF SOIL AND WATER CONSERVATION ENGINEERING

COLLEGE OF AGRICULTURAL ENGINEERING AND TECHNOLOGY

DR. BALASAHEB SAWANT KONKAN KRISHI VIDYAPEETH

DAPOLI- 415 712, DIST. RATNAGIRI, M. S. (INDIA)

2015

iii

CANDIDATE‟S DECLARATION

I hereby declare that this thesis or part thereof has not been submitted

by me or any other person to any other

University or Institute

for a Degree or

Diploma.

Place: CAET, Dapoli (Swapnil Ajay Kothe)

Dated: / /2015

iv

Dr. B. L. Ayare

B. Tech. (Agril. Engg.), M. Tech. (WRDM), Ph.D (SWCE)

Agricultural Engineer,

AICRP on Water Management, Wakawali,

Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli – 415 712

Dist. Ratnagiri, Maharashtra State (India).

CERTIFICATE

This is to certify that the thesis entitled “DEVELOPMENT OF RAINFALL–

RUNOFF MODEL USING ARTIFICIAL NEURAL NETWORK MODEL FOR A

PRIYADARSHINI WATERSHED”, submitted to Faculty of Agricultural Engineering, Dr.

Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli, Dist. Ratnagiri (Maharashtra State) in

partial fulfillment of the requirements for the award of the degree of Master of Technology

(Agricultural Engineering) in Soil and Water Conservation Engineering, embodies the

record of a piece of bonafied research work carried out by Mr. Swapnil Ajay Kothe under

my guidance and supervision. No part of this thesis has been submitted for any other degree,

diploma or publication in any other form.

The assistance and help received during the course of this investigation and source of

the literature have been duly acknowledged.

Place : CAET, Dapoli (B. L. Ayare)

Date : / / 2015

v

Prof. dilip MAHALE

B. Tech. (Agril. Engg.), M. Tech. (SWCE)

Professor and Head,

Department of Soil and Water Conservation Engineering,

College of Agricultural Engineering and Technology,

Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli – 415 712


CERTIFICATE

This is to certify that the thesis entitled “DEVELOPMENT OF RAINFALL–







the guidance and supervision of Dr. B. L. Ayare, Agricultural Engineer, AICRP on Water

Management, Wakawali, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli. No part

of the thesis has been submitted for any other degree, diploma or publication in any other

form.

The assistance and help received during the course of this investigation and source of

the literature have been duly acknowledged.

Place : CAET, Dapoli (dilip MAHALE)

Date : / / 2015

vi

Dr. N. J. Thakor

B.Tech (Agril. Engg.), M. Tech (IIT), Ph.D (Canada), FIE, FISAE

Associate Dean,


Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli – 415712.


CERTIFICATE

This is to certify that the thesis entitled “DEVELOPMENT OF RAINFALL-







the guidance and supervision of Dr. B. L. Ayare, Agricultural Engineer, AICRP on Water

Management, Wakawali, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli. No part

of the thesis has been submitted for any other degree, diploma or publication in any other

form.

The assistance and help received during the course of this investigation and source

of the literature have been duly acknowledged.

Place : CAET, Dapoli (N. J. Thakor)

Dated : / / 2015

vii

ACKNOWLEDGEMENT

„The programmatic approach to problem must play a certain role in development

initiatives‟. My guide who made us to realize this thing, Dr. B. L. Ayare, Agricultural

Engineer, AICRP on Water Management, Wakawali, Dr. Balasaheb Sawant Konkan Krishi

Vidyapeeth, Dapoli assisted me to construct every stepping-stone leading towards this project

success. I seize this opportunity to express my frequent puissant and scholastic guidance,

immutable interest, constructive criticism and timely help.

I feel deep sense of gratitude to express my heartful thanks to Dr. N. J. Thakor,

Associate Dean, Faculty of Agricultural Engineering and Technology, Dr.B.S.K.K.V., Dapoli,

for his rewarding guidance and kind help in providing necessary facility in time made the

research study and achievement.

I am also highly obliged to Prof. dilip. MAHALE, Professor and Head, Department

of Soil and Water Conservation Engineering, College of Agricultural Engineering and

Technology, Dapoli for his valuable guidance, timely suggestion and constant

encouragement.

I am equally indebted to, Dr. S. B. Nandgude, Associate Professor, and Prof. H. N.

Bhange, Assistant Professor, Department of Soil and Water Conservation Engineering,

College of Agricultural Engineering and Technology, Dapoli for arousing our interest and

amending the work timely.

I am also indebted to Prof. S. T. Patil, Assistant Professor, Department of Irrigation

and Drainage Engineering, College of Agricultural Engineering and Technology, Dapoli for

his valuable suggestions and guidance for my research work. I would loose no opportunity to

express our sincere thanks to Mrs. S. S. Nagarkar, Senior Research Assistant, Department of

Soil and Water Conservation Engineering, College of Agricultural Engineering and

Technology, Dapoli for helping soil analysis. I am also thankful to Mr. S. S. Idate,

Laboratory Assistant, Department of Soil and Water Conservation Engineering.

It is my best privilege to express my sincere thanks to Dr. U. S. Mahadkar, Professor

and Head, Department of Agronomy, College of Agriculture, Dr. Balasaheb Sawant Konkan

Krishi Vidyapeeth, Dapoli, for providing rainfall data for the year 2014 and guidance during

project work.

Our devout gratitude is towards all staff members of College of Agriculture

Engineering and Technology, Dapoli and all my colleagues, who have directly and indirectly

helped me to carry out work effectively.

viii

I wish to employ this opportunity to annotate my unsociable regards, thanks and heart

felt good wishes to Miss. Mehendale G. M. for her co-operation for guiding and solving

software problem during my research work.

I fall short of words in expressing my thanks to my dear friends Jagruti, Pradeep sir,

Ganesh, Amit, Mady, Balaji, Mahesh, Sunil and all my senior and juniors for their constant

support and timely help during this project work.

I am also very thankful to my friends and seniors Aniket, Prashant, Sagar sir and

Swapnil sir for providing me research paper during my research work.

I am extremely obliged to acknowledge the love and affection of my beloved parents

Mummy and Pappa, Ajji and elder sister Shubhangi. No words are enough to describe their

efforts in building up my educational career and financial support whenever required. How

can one express complete thanks for the efforts they have taken right from spoon-feeding in

the childhood to the last moment just passed.

Our life is with a “Golden Line” today and I dedicate it to our Associate Dean who

has laid a perfect foundation, by showing me path bright future.

I express my sincere thanks to whom directly and indirectly extended help during the

research work.

Thank You!

Place: CAET, Dapoli (Kothe Swapnil Ajay)

Dated: / /2015

ix

TABLE OF CONTENTS

Sr.

No.

Title Page No.

CANDIDATE‟S DECLARATION iii

CERTIFICATES

1. Research Guide iv

2. Head of Department v

3. Associate Dean vi

ACKNOWLEDGEMENT vii-viii

TABLE OF CONTENTS ix-x

LIST OF TABLES xi

LIST OF FIGURES xii-xii

LIST OF SYMBOLS xiv

LIST OF ABBREVIATIONS xv

ABSTRACT xvi-xvii

1. INTRODUCTION 1-3

2. REVIEW OF LITERATURE 4-9

3. MATERIAL AND METHODS 10-26

3.1 General Description 10

3.1.1 Study area 10

3.1.2 Data collection 10

3.1.3 Runoff Estimation 10

3.2 Pre-analysis and Formulation of Input and Output Data 12

3.3 Software used 12

3.4 Artificial Neural Networks (ANNs) 12

3.4.1 General Information 12

3.4.2 The Biological Neurons 12

3.4.3 Basic concept of Artificial Neural Network (ANN) model 13-17

3.4.3.1 Activation function 14

3.4.3.2 The back propagation algorithm 16

3.4.3.3 Procedure for ANN model simulation 17

3.5 Training procedure for neural network 18-25

3.6 Statistical analysis 25-26

4. RESULTS AND DISCUSSION 27-46

4.1 Runoff Estimation by using Rectangular weir 27

4.2 Runoff Estimation by using ANN Model 28

4.3 ANN with one input 29-42

x

4.3.1 Most Suitable ANN Architectures 32-42

4.4 Observed and Predicted Runoff 43

4.5 Statistical analysis by ANN method 49

5. SUMMARY AND CONCLUSIONS 50-52

5.1 Summary 50-51

5.2 Conclusions 51-52

6. BIBLIOGRAPHY 53-55

7. APPENDICES 56-60

APPENDIX-I

xi

LIST OF TABLES

Table No. Title

Page No.

4.1 Monthly rainfall and runoff observed at Priyadarshini

watershed for year 2010, 2011, 2013 and 2014

27-28

4.2

Statistical performance of various ANN architectures. 29-31

4.3

Most suitable ANN architecture based on statistical

performance

32

4.4 Observed and Predicted Runoff data for 1-48-1 architecture. 43-49

7.1 Four years rainfall- runoff data of Priyadarshini watershed. 56-60

xii

LIST OF FIGURES

Figure

No.

Title Page

No.

3.1 Location map of Priyadarshini watershed 11

3.2 Structure of a biological neuron 13

3.3 Training network inside the neural network 14

3.4 Log sigmoidal transfer function 15

3.5 Architecture of an artificial neuron 16

3.6 Architecture of feed forward multilayer perception (MLP) 17

3.7 Schematic Representation of Artificial Neural Network 18

3.8 Opening window of Matlab 7.9 ANN toolbox 19

3.9 Neural network fitting tool window 19

3.10 Importing of data to Matlab software 20

3.11 Actual importing data window 20

3.12 Dividing window into training, validation and testing 21

3.13 Selection of number of neuron window 21

3.14 Train data by using Levenberg-Marquardt Algorithm 22

3.15 Window displays the progress of network 22

3.16 Saving the results 23

3.17 Performance plot window 23

3.18 Training state window 24

3.19 Function to fit window 24

3.20 Regression plot of neural network 25

4.1 Hydrograph of Date versus observed runoff and predicted runoff for 1-

18-1 architecture

33

4.2 Scatter plot of observed runoff versus predicted runoff for 1-18-1

architecture

33


22-1 architecture

34

4.4 Scatter plot of observed runoff versus predicted runoff for 1-22-1 34

xiii

architecture


32-1 architecture

35


architecture

35


34-1 architecture

36


architecture

36


35-1 architecture

37


architecture

37


40-1 architecture

38


architecture

38


41-1 architecture

39


architecture

39


45-1 architecture

40


architecture

40


48-1 architecture

41


architecture

41


65-1 architecture

42


architecture

42

xiv

LIST OF SYMBOLS

Symbol Meaning

& And

cm Centimeters

0 C Degree Celsius

0 E East longitude

ha Hectare

hrs Hours

km Kilometer

lit/sec Litre per second

m Meter

mm Millimeter

Mha Million hectare

Mha-m Million hectare per meter

0 N

North longitude

% Percent

m2

Square meter

xv

LIST OF ABBREVIATIONS

Abbreviations Meaning

ANN Artificial neural network

ASCE American Society of Civil Engineers

bn Billion

C. A. E. T. College of Agricultural Engineering and

Technology

CMS Cubic meter per second

COR Correlation

Dr. BSKKV Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth

et al. And other

etc. Etcetera

Engg. Engineering

Fig. Figure

GDP Gross Domestic Product

GRNN Generalised Regression Neural Network

i.e. That is

MARE Mean Absolute Relative Error

MSE Mean Square Error

pp Page number

r Correlation coefficient

R2

Coefficient of Determination

RCC Reinforced Cement Concrete

RMSE Root Mean Square Error

SOM Self Organising Map

Sq. km Square Kilometer

TBPNN Temporal Back Propagation Neural Network

UK United kingdom

UN United Nations

UNICEF United Nations Children‟s Fund

xvi

ABSTRACT

“DEVELOPMENT OF RAINFALL – RUNOFF MODEL USING

ARTIFICIAL NEURAL NETWORK FOR A PRIYADARSHINI

WATERSHED”

by

Swapnil Ajay Kothe


Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Dapoli.

Dist. Ratnagiri, Maharashtra State (India)

Research Guide : Dr. B. L. Ayare

Department : Soil and Water Conservation Engineering

An artificial neural network is massively parallel distributed information processing

system that has certain characteristics resembling biological neural network of human being.

Artificial Neural Network (ANN) models have been used successfully to model complex non-

linear input-output relationships in an extremely interdisciplinary field. Artificial Neural

Networks (ANNs) have been used for modelling complex hydrological process, such as

rainfall-runoff and have been shown to be one of the most promising tools in Hydrology.

Hydrological modeling is a powerful technique of hydrologic system investigation for both

the research hydrologists and the practicing water resources engineers involved in the

planning and development of integrated approach for management of water resources. In this

project, the observed rainfall and runoff data of four years (i.e. 2010, 2011, 2013 and 2014)

were used as input data for study. In ANN, input data was divided in three segment 70 per

cent, 15 per cent and 15 per cent for training, validation and testing purpose respectively.

Rainfall-runoff models play an important role in water resource management planning. Total

70 numbers of different types of models with various degrees of complexity have been

developed for this purpose. The output from ANN was statistically tested with statistical

parameters, i.e. Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE),

Coefficient of Determination (R2) and Correlation (r). The models with single input were not

performing well. Total 10 best suitable architectures selected from the 70 model architecture

xvii

which were studied. The 10 best suitable architectures were 1-18-1, 1-22-1, 1-32-1, 1-34-1, 1-

35-1, 1-40-1, 1-41-1, 1-45-1, 1-48-1, 1-65-1. ANN with 1-48-1 architecture is found to be

most suitable which gives 13.4597, 472.0690, 0.8376 and 0.9188 values for Root Mean

Square Error, Mean Absolute Error, Coefficient of Determination (R²) and Correlation (r)

respectively. ANN 1-48-1 architectures can be adopted to estimate runoff from ungauged

watershed with that day rainfall as single input. The result of this project has shown that with

combination of computational efficiency measures and ability of input parameters describes

the physical behaviour of hydro-climatologic variables. Improvement of the model

predictability is possible in artificial neural network environment, with improved structures of

more input, more hidden layer and hidden neurons.

xviii

CHAPTER I

INTRODUCTION

Water is essential for life. Rainfall is vital resource of water. It is also one of the prime

requirements for agriculture, industrial, domestic and recreational activities. Components of

precipitation, resolved into soil moisture and groundwater are the prerequisites for biomass

production and social development in dry areas. The world‟s total water resources are

estimated as 1.36 × 108 M ha-m. About 97.2 per cent of these world water resources are

saline water mainly in oceans, and only 2.8 percent is available as freshwater at any time on

the planet earth. Out of this 2.8 per cent of fresh water, about 2.2 per cent is available as

surface water and 0.6 per cent as ground water. Even out of this 2.2 per cent of surface water,

2.15 per cent is fresh water in glaciers and icecaps and only of the order of 0.01 per cent is

available in lakes and streams, the remaining 0.04 per cent being in other forms. Out of 0.6

per cent of stored ground water, only about 0.25 per cent can be economically extracted with

the present drilling technology. So, rainfall is cheap and prime source of fresh water.

Per capita availability of water is reducing at an alarming rate. In 1950 water

availability per capita was 6042 cubic meter which is reduced to 1545 cubic meter in 2011. It

has been estimated by UN that it will be reduced to 1140 cubic meter in 2050. As per

UNICEF, 2013 estimates 3.4 bn population will be faced with water scarcity in 2025, nearly

40 per cent of the world population will face water scarce in 2050. About 20 per cent of the

world‟s aquifer will be depleted in 2050. It shows that water will be a serious issue in future.

India occupies only 3.28 million sq. km geographical area, which is 2.4 per cent of the

world‟s land area; it supports over 17 per cent of the world‟s population with 4 per cent world

water resources. India also has a livestock population of 500 million, which is about 20 per

cent of the world‟s total livestock population. More than half of these are cattle, forming the

backbone of Indian agriculture. Indian agriculture shows 14.1 per cent share of the total GDP.

Rainfall runoff relationship is an essential component in the process of water resources

evaluation and is considered as a central problem in hydrology. There have been extensive

researches conducted on the rainfall runoff relationship with different methods by various

scientists. The adoption of artificial neural network has added a new dimension to the system

theoretic modelling approach (ASCE, 2000 (a, b)).

Therefore, it is necessary to the estimate runoff in un-gauged watershed for the design

of hydraulic structures, soil conservation structures, water harvesting structures, flood

moderation studies and design of drainage systems etc. A rainfall-runoff model is a

xix

mathematical formulae describing the rainfall - runoff relations of a catchment area. More

precisely, it produces the surface runoff hydrograph as a response to a rainfall hydrograph as

input. In other words, the model calculates the conversion of rainfall into runoff. A rainfall

runoff model can be really helpful in the case of calculating discharge from a basin. The

transformation of rainfall into runoff over a catchment is known to be very complex

hydrological phenomenon, as this process is highly nonlinear, time-varying and spatially

distributed. Over the years researchers have developed many models to simulate this process.

Based on the problem statement and on the complexities involved, these models are

categorized as empirical, black-box, conceptual or physically-based distributed models. The

unit hydrograph, which is a linear rainfall-runoff model is one well-known example of such a

relationship. However, these simpler models normally fail to represent the nonlinear dynamics

inherent in the process of rainfall-runoff transformation which can be done by using Artificial

Neural Networks and fuzzy logic (Rajurkar et al., 2004).

Hydrological modeling is a powerful technique of hydrologic system investigation for

both the research hydrologists and the practicing water resources engineers involved in the

planning and development of integrated approach for management of water resources.

Prediction of runoff is one of the most useful hydrological systems. The prediction may be

used to assess or predict aspects of flooding, aid in reservoir operation, or be used in the

prediction of the transport of water born contamination. Rainfall-runoff models play an

important role in water resource management planning and therefore, different types of

models with various degrees of complexity have been developed for this purpose. Conceptual

rainfall-runoff models have been widely employed in hydrological modeling. Some of the

well-known conceptual models include the Stanford Watershed Model (SWM), the

Xinanjiang Model and the Soil Moisture Accounting and Routing (SMAR) Model. Although

the modelling of runoff has been studied, many aspects of its dynamics are still unclear.

An artificial neural network is massively parallel distributed information processing

system that has certain characteristics resembling biological neural network of human being.

Artificial Neural Network (ANN) models have been used successfully to model complex non-

linear input-output relationships in an extremely interdisciplinary field. The natural behaviour

of hydrological processes is appropriate for the application of ANN method. In recent years,

ANNs have been used intensively for prediction and forecasting in a number of water-related

areas, including water resource study (El-Shafie et al., 2007), prediction of evaporation

(Sudheer et al., 2002), hydrograph simulator, rainfall forecasting. Hence, motivated by the

successful applications in modeling non-linear system behaviors in a wide range of areas, this

xx

study demonstrated the application of Artificial Neural Network (ANN) to predict rainfall-

runoff relationship. Konkan region is long narrow strip on western side of Sahyadri ranges

along 720 Km coastline. It spread between 15º 6' N and 20º 22' N latitude and 72º 39' E and

73º 48' E longitude, covering total geographical area of 3.04 Mha. Konkan region receives

heavy rainfall (annual average rainfall 280 cm) in the monsoon season, but faces scarcity of

water for drinking purposes in the month of April to May. The area under irrigation of the

region is very meager (less than 4 per cent). This is due to undulating terrain with general

slope ranging from 7 to 35 percent and covered with shallow and lateritic soils (64 %), having

low moisture holding capacity and high runoff. Hence, by considering the facts, the present

study was conducted with following objective:-

1) Rainfall-Runoff analysis of Priyadarshini Watershed.

2) Development of rainfall-runoff model using Artificial Neural Network for

Priyadarshini Watershed.

xxi

CHAPTER II

REVIEW OF LITERATURE

This chapter deals with review of literature covering aspect of Rainfall-Runoff

Modeling using an Artificial Neural Network Model.

2.1 Rainfall-Runoff modelling using Artificial Neural Network.

Minns and Hall (1996) studied artificial neural networks as rainfall-runoff model.

They studied a series of numerical experiments, in which flow data were generated from

synthetic storm sequences routed through a conceptual hydrological model consisting of a

single nonlinear reservoir, has demonstrated the (ANNs). Trial with both one and two hidden

layers in the ANN have shown that, although improved performances are achieved with the

extra hidden layer, the additional computational effort does not appear justified for data sets

exhibiting the degree of nonlinear behavior typical of rainfall and sequences from many

catchment areas.

Dawson and Wilby (1998) reviewed an artificial neural network approach to rainfall

runoff modelling. This paper provides a discussion of the development and application of

Artificial Neural Network (ANNs) to flow forecasting in two flood-prone UK catchments

using real hydrometric data. Comparisons were made between the performance of the ANN

and those of conventional flood forecasting systems. The results obtained for validation

forecasts were of comparable quality to those obtained from operational systems for the River

Amber. The ability of the ANN to cope with missing data and to "learn” from the event

currently being forecast in real time makes it an appealing alternative to conventional lumped

or semi-distributed flood forecasting models. However, further research is required to

determine the optimum ANN training period for a given catchment, season and hydrological

contexts.

Sajikumar and Thandaveswara (1999) studied a non-linear rainfall-runoff model using

an artificial neural network. A rainfall- runoff model that can be successfully estimated (i.e.

yielding sufficiently accurate results) using relatively short lengths of data, is desirable for

any basins in general, and the basins of developing countries like India. An artificial neural

network paradigm, known as the temporal back propagation neural network (TBP-NN), is

successfully demonstrated as a monthly rainfall-runoff model in a “scarce data” scenario (i.e.

the effects of using reduced calibration periods on the performance) is compared with

Volterra type Functional Series Models (FSM), utilizing the data of the River Lee (in the UK)

xxii

and the Thuthapuzha River (in Kerala, India). The results confirm the TBN-NN model as

being the most efficient of the black models tested for calibration periods as short as six years.

Maier and Dandy (2000) studied neural networks for the prediction and forecasting of

water resources variables: a review of modeling issues and applications. A review of 43

papers dealing with the use of neural network models for the prediction of forecasting of

water resources variables is undertaken in terms of the modeling process adopted. In all but

two of the papers reviewed, feed forward networks are used. The vast majority of these

networks are trained using the back propagation algorithm. The process of choosing

appropriate stopping criteria & optimising network geometry and internal network parameters

is generally described poorly or carried out inadequately. All of the above factors can result in

non-optimal model performance and an inability to draw meaningful comparisons between

different models.

Sudheer et al. (2002) studied data-driven algorithm for constructing artificial neural

network rainfall-runoff models. The method studies the statistical properties such as cross,

auto and partial- auto-correlation of the data series in identifying a unique input vector that

best represents the process for the basin, and standard algorithm for training. The

methodology has been validated using the data for a river basin in India. The results of the

study are highly promising and indicated that it could significantly reduce the effort and

computational time required in developing an ANN model.

Riad and Mania (2004) has studied Rainfall-Runoff Model Using an Artificial Neural

Network Approach. An ANN was developed and used to model the rainfall-runoff

relationship, in a catchment located in a semiarid climate Ourika basin at Agbalou in

Morocco. The multilayer perceptron (MLP) neural network was chosen for use in that study.

The results and comparative study indicate that the artificial neural network method was more

suitable to predict river runoff than classical regression model.

Rajurkar et al. (2004) studied modeling of the daily rainfall-runoff relationship with

artificial neural network. An approach for modeling daily flows during flood events using

artificial neural network (ANN) is presented. The rainfall runoff process is modeled by

sampling a simple linear (black box) model with the ANN. The study uses data from two

large size catchments in India and five other catchments used earlier by the World

Meteorological Organization (WMO) for inter comparison of the operational hydrological

model. The study demonstrate that the approach adopted herein for modeling produces

reasonably satisfactory results for data of catchment from different geographical locations,

which thus proves its versatility.

xxiii

Avinash et al. (2006) studied Simulation of Runoff and Sediment Yield using

Artificial Neural Networks. They has studied Daily, weekly, ten-daily, and monthly monsoon

runoff and sediment yield from an Indian catchment were simulated using back propagation

artificial neural network (BPANN) technique, and the results compared with the observed and

with those due to single- and multi-input linear transfer function models. Normalising the

input by its maximum for both the pattern and batch learning algorithms in BPANN, the

model parsimony was achieved through network pruning utilising error sensitivity to weight a

criterion, and it was generalised through cross-validation. The performance based on

correlation coefficient and coefficient of efficiency suggested the pattern-learned artificial

neural network (ANN) based runoff simulation to be superior to both single- and multi-input

models in calibration. The single-input models were however superior in verification. The

ANN based sediment-yield models performed better than both single- and multi-input models

in calibration as well as cross-validation/verification.

Chen and Adams (2006) studied integration of artificial neural networks with

conceptual models in rainfall-runoff modeling. A hybrid form of rainfall-runoff models that

integrated artificial neural networks (ANNs) with conceptual models is proposed in that

study. Based on this integrated approach, the spatial variation of rainfall, the homogeneity of

watershed characteristics and their impacts on runoff can be investigated to the development

of a semi-distributed form of conceptual rainfall-runoff models. As a result in each catchment,

the runoff generation and water budget among different runoff components including surface

runoff and groundwater can be simulated with consideration of the spatially distributed model

parameters and rainfall inputs in the runoff routing. Instead of a linear superposition of the

routed runoff output at the entire watershed outlet as traditionally performed in a semi-

distributed form of conceptual models, artificial neural networks as effective tools in

nonlinear mapping are employed to explore nonlinear transformation of the runoff generated

from the individual sub catchment into the total runoff at the entire watershed outlet. The

verification results from the three conceptual models indicated that the approach of

integrating artificial neural network with conceptual models presented and that shows promise

in rainfall-runoff modeling.

Jain and Shrinivasulu (2006) studied integrated approach to model decomposed how

hydrograph using artificial neural network and conceptual techniques. The results obtained in

that study indicate that (a) the rainfall-runoff relationship in a large catchment consists of at

least three or four different mappings corresponding to different dynamics of the underlying

physical processes, (b) an integrated approach that models the different techniques is better

xxiv

than a single ANN in modeling the complex, dynamic, non-linear, and fragmented rainfall

runoff process, (c) a simple model based on the concept of flow recession is better than as

ANN to model the falling limb of a flow hydrograph, and (d) decomposing a flow hydrograph

into the different segments corresponding to the different dynamics based on the physical

concepts is better than using the soft decomposition employed using SOM.

Kalteh (2008) illustrated rainfall-runoff modelling using artificial neural networks

(ANNs): Modeling and understanding. The results indicate that ANNs are promising tools not

only in accurate modeling of complex processes but also in providing insight from the learned

relationship, which would assist the modeler in understanding of the process under

investigation as well as in evaluation of the model.

Yazdani et al. (2009) studied monthly runoff estimation using Artificial Neural

Network (ANN). In that study ANN model was employed for runoff estimation in Plaszjan

River basin in the central part of Iran. The models used are Multiple Perceptron (MLP) and

Recurrent Neural Network (RNN). Different topologies of Neural Networks were created with

change in input layers, nodes as well as in the hidden layer. The best architecture was found

as 7.4.1. Recurrent Neural Network led to better results than Multilayer Perceptron Network.

Also results indicated that ANN is an appropriate technique for monthly runoff estimation in

the selected basin with these networks being also of the capability to show basin response to

rainfall events.

Arslan (2011) has studied Rainfall–Runoff Modeling Based on Artificial Neural

Networks (ANNs). They has studied, the influences of back propagation efficiencies and

enabling /disabling of input dimensions on rainfall –runoff modelling capability of the

artificial neural network was applied by trying different input dimension for Khasa Chai

catchments, this was done for the evaluation of modeling rainfall-runoff in this region.

Twelve model structures were developed, each one with different number of neurons in the

hidden layer to investigate the probability impacts of enabling /disabling rainfall-runoff,

rainfall, average air temperature, evaporation, humidity. For each model the most successful

structure was found depending on the value of correlation coefficient R and the value of mean

square errors MSE at validation stage. The best rainfall-runoff model for Khasa Chai

catchments was concluded with nine input dimension, nine neurons in the hidden layer. The

results of this research has shown that with combination of computational efficiency measures

and ability of input parameters which describe the physical behavior of hydro-climatologic

variables, improvement of the model predictability is possible in artificial neural network

environment.

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El-shafie et al. (2011) has studied Performance of Artificial Neural Network and

Regression Techniques for Rainfall-Runoff Prediction. They aimed to utilize an Artificial

Neural Network (ANN) to predict the rainfall-runoff relationship in a catchment area located

in a Tanakami region of Japan. The study illustrates the applications of the feed forward back

propagation with hyperbolic tangent neurons in the hidden layer and linear neuron in the

output layer was used for rainfall prediction. To evaluate the performance of the proposed

model, three statistical indexes were used, namely; Correlation coefficient (R), mean square

error (MSE) and correlation of determination (R2). The results showed that the feed forward

back propagation Neural Network (ANN) can describe the behaviour of rainfall-runoff

relation more accurately than the classical regression model.

Joshi and Patel (2011) reviewed rainfall-runoff modeling using artificial neural

network (A literature Review). Three neutral network methods, Feed Forward Back

Propagation (FFBP), Radial Basis Function (RBF) and Generalized Regression Neural

Network (GRNN) were employed for rainfall-runoff modeling of Maleshri hydro

meteorological data. It was seen that all three different ANN algorithms compared well with

conventional Multi Linear Regression (MLR) technique. It was seen that only GRNN

technique did not provide negative flow estimations for some observations. The rainfall-

runoff correlograms was successfully used in determination of the input layer node number.

Sarkar and Kumar (2012) have studied Artificial Neural Networks for Event Based

Rainfall-Runoff Modeling. They had examined its applicability to model the event-based

rainfall-runoff process. A case study has been done for Ajay river basin to develop event-

based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site.

The results demonstrate that ANN models are able to provide a good representation of an

event-based rainfall-runoff process. The two important parameters, when predicting a flood

hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The

developed ANN models have been able to predict this information with great accuracy. They

shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for

determining the peak discharge and time to the peak discharge very accurately.

Singh et al. (2013) has studied Artificial Neural Networks Based Daily Rainfall-

Runoff Model for an Agricultural Hilly Watershed. In that study, ANN based daily rainfall-

runoff model has been established for an agricultural hilly watershed known as Khunt micro-

watershed located in the district of Almora, Uttarakhand, India. In the development of the

model, daily rainfall and runoff data for the period 1st June to 31th October for years 2005-

2009 were used to train the ANN, and for the years 2010 and 2011 were used for model

xxvi

validation purpose. The performance of the developed model was assessed based on

parameters like root mean square error (RMSE) and correlation coefficient (R). A network

structure resulting in highest value of correlation coefficient and simultaneously in the lowest

value of RMSE was designated as the best performing. Based on these considerations, it was

observed that the performance of the model based on one day lag and two days lag time were

found satisfactory for the study area. However, the model based on the network 5-5-1

structure with 2 days lag was found to have an edge over the model based on 3-3-1 model

structure with one day lag.

Nandgude et al. (2014) has studied Rainfall- Runoff modeling of Small Watershed in

Konkan Region Using Artificial Neural Network. They had concluded that the rainfall-runoff

relationship is one of the most complex hydrologic phenomena and it is based on tremendous

spatial and temporal variability of watershed characteristics, precipitation patterns etc.

Therefore other models were not performing well. ANN 1-12-1 architectures can be adopted

to estimate runoff from un-gauged watershed with rainfall as input. Controlling the runoff

would require a complete assessment of soil erosion and associated non-point source pollution

impacts in the watershed from a long-term perspective.

xxvii

CHAPTER III

MATERIALS AND METHODS

The main purpose of this study is to develop Artificial Neural Networks for

forecasting runoff. This chapter contains the location and climate of study area, collection of

meteorological data, methodology adopted for rainfall modelling using Artificial Neural

Networks (ANNs) models. Procedure used for calibration and validation of the model and

various criteria for evaluating performance of the models.

3.1 General Description

3.1.1 Study area:

The research work was carried out at the Priyadarshini watershed, College of

Agricultural Engineering and Technology, Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth,

Dapoli, Dist- Ratnagiri (M.S.).

The Priyadarshini Watershed is located at 17.1° N latitude, 73.26° E longitudes and

250 m above mean sea level. The region comes under heavy rainfall with average annual

rainfall of 3500 mm. Priyadarshini watershed has 38.72 ha area. The ambient temperature of

the region varies from 7.5 0C to 38.5

0C and relative humidity varies from 55 percent to 99

percent in different seasons. The climate of the region is hot and humid. The region has hilly

topography with highly drainable lateritic type soils. The location of study area is shown in

Fig. 3.1.

3.1.2. Data collection:

Daily rainfall data has been collected from Department of Agronomy, College of

Agriculture, Dapoli.

3.1.3. Runoff:

Runoff was measured at the outlet of Priyadarshini Watershed for a period of June-

October 2010, 2011, 2013 and 2014 by using rectangular weir (RCC nala bandh) with end

contractions at both ends. The formula for computation of discharge through rectangular weir

(RCC nala bandh) with end contractions at both ends is given by the equation 3.1. (Bansal R.

K., 2010)

Q = 0.0184 (L – 0.2H) × H3/2

…(3.1)

Where,

Q = Discharge, lit/sec

L = Length of crest, cm

H= Head over crest, cm

xxviii

Fig. 3.1 Location map of Priyadarshini Watershed.

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3.2 Pre-analysis and Formulation of Input and Output Data

The daily meteorological data of four years (i.e. 2010, 2011, 2013 and 2014) were

collected from Department of Agronomy, College of Agriculture, Dapoli. Due to some

technical problem of instrument, the runoff data of 2012 was not taken. Thus the total number

of samples for four year‟s period was 198. Each 198 samples of observed rainfall and

observed runoff were taken as input data and output data respectively for analysis and model

development purpose.

These 198 samples were distributed as 138 samples (70%) for training, 30 samples

(15%) for validation and 30 samples (15%) for testing purpose.

3.3 Software:

Model has been developed using MATLAB 2009 software available in the laboratory

of Soil and Water Conservation Engineering, College of Agricultural Engineering and

Technology, Dr. B. S. K. K. V., Dapoli.

3.4 Artificial Neural Networks (ANNs)

3.4.1 General

Artificial Neural Networks (ANNs) are inspired by the structure of human brain that is

well suited for complicated task such as runoff prediction, rainfall-runoff modelling, river

flow prediction etc., in hydrologic systems. These neurons were presented as models of

biological neurons and as conceptual components that could perform conceptual task. ANN

has been proven to provide better solutions for simulations and forecasting. Before looking at

the structure of ANN, we need to understand the structure of biological neurons.

3.4.2 The Biological Neurons

The human brain is the most complex computing known device. The brain‟s powerful

thinking of remembering, and problem solving capabilities inspired many scientists to attempt

computer modelling of its operation. There is a close analogy between the structure of a

biological neuron (i.e. a brain or nerve cell) and the processing element (or artificial neurons).

The biological neuron has three types of components that are of particular interest in

understanding an artificial neuron: its dendrites, cell body and axon (Fig. 3.2). The signals are

electric impulses that are transmitted across a synaptic gap by means of chemical process, a

shown in Fig. 3.2. The cell body sums the incoming signals. When sufficient input is

received, the cell fires; that is, it transmits a signal over its axon to other cells. It is often

xxx

supposed that a cell either fires or doesn‟t at any instant of time, so that transmitted signals

can be treated as binary.

Fig. 3.2 Structure of a biological neuron

The transmission of the signal from a particular neuron is accomplished by an action

potential resulting from differential concentration of ions on either side of the neuron‟s axon.

The ions most directly involves are potassium, sodium and chloride. It causes the neuron to

fire producing an output signal. The output signal travels along the axon to other receiving

neurons. The magnitude of the signal sent depends on the amount of chemical released by the

axon and receive by the dendrites.

3.4.3 Basic concept of Artificial Neural Network (ANN) model

Artificial neural network (ANN) is a massively parallel distributed information

processing system that has certain performance characteristics resembling biological neural

network of the human brain (Junsawang et al., 2007). ANN has been developed from a

generalization of mathematical model of human cognition or neural biology. Their

development is based on the following rules:

1. Information processing occurs at many single elements called nodes, also referred to as

units, cells of neurons.

2. Signals are passed between nodes through connection links.

3. Each connection link has as associated weight that represents its connection strength.

4. Each node typically applies a nonlinear transformation called an activation function to

its net input to determine its output signal.

An ANN is a highly interconnected network of many simple processing units called

neurons, which are analogous to the biological neurons in the human brain. The basic building

block of an ANN is the neurons. They receive an input and produced an output, which is to be

xxxi

passed to other neurons in other layers. Neurons having similar characteristics in an ANN are

arranged in groups called layers. The neurons in one layer are connected to those in the

adjacent layers, but not to those in the same layer. The strength of connection between the

neurons in adjacent layers is represented by what is known as „connection strength‟ or

„weights‟.

An ANN normally consists of three layers, an input layer, a hidden layer and an

output layer. Input layer usually receives the input signal values. Neurons in output layer

produce the output signal. ANN is essentially useful for modeling and prediction of uncertain

and complex phenomena. A neural network can be trained from the previous data to forecast

future events, without accurately understanding the physical parameters which influences the

presents and future events. The training network performed in the neural network is shown in

Fig. 3.3:

Fig. 3.3 Training network inside the neural network.

The objective of the present study is to simulate rainfall runoff relationship using

ANN models. The relationship of rainfall-runoff is known to be highly nonlinear and

complex. The rainfall-runoff relationship is one of the most complex hydrologic phenomena

and it is based on tremendous spatial and temporal variability of watershed characteristics,

precipitation patterns etc. Therefore other models were not performing well. Hence it is

needed to study the ANN structure to simulate runoff from rainfall data for particular soil

conservation measure and different cropping pattern in ungauged watershed.

3.4.3.1 Activation function

The activation function of a neuron in a neural network is only processing function. It

is utilized for the limiting the amplitude of the output of a neuron, also known as transfer

xxxii

function is referred to as squashing function as quashes (limits) the permissible amplitude

range to some finite value. It gives in a range of 0 to 1 (Fig 3.4).

a= log sig (n)

Fig. 3.4 Log sigmoidal transfer function.

This activation function is commonly used in the hidden layers of multiplayer ANN

network and it is represented by equation 3.2. The symbol in the square to the right of each

transfer function. These icons replace the general in the network diagram blocks to the

particular activation function being used.

The mathematical expression of the logistic function is given by the equation 3.2.

( )

…(3.2)

An attempt to improve the accuracy is to use data on discharge excess and sum of

rainfall during the last 24 hours from the prediction time is additional input to the network

model.

Other sigmoidal functions also used and they are given by the equation 3.3 and

equation 3.4.

i) Linear function

( )

ii) Hyperbolic tangent equation

( ) ( )

( ) ( ) ( )

xxxiii

3.4.3.2 The back propagation algorithm

In the present study the back propagation algorithm (Rumelhart and McClelland,

1986) is used in feed –forward ANNs. This means that artificial neurons are organized in

layers and send their signals “forward” and then the error are propagated backwards. The

network receives inputs by neurons in input layer and the output of network is given by the

neurons on an output layer. There may be one or more intermediate hidden layers. The back

propagation algorithm uses supervised learning, which means that provides the algorithm with

examples of inputs and outputs we want the network to compute, and then the error

(difference between actual and expected results) was calculated. The idea of back propagation

algorithm was to reduce this error, until the ANN learns the training data. The training begins

with random weights, and the goal was to adjust them so that the error will be minimum. The

activation function of artificial neurons in ANNs implementation the back propagation

algorithm is a weighted sum (the sum of inputs X multiplied by their respective weights w).

Architecture of artificial neurons is as shown in Fig. 3.5 and architecture of feed forward

multilayer perception is shown in Fig. 3.6:

Fig. 3.5 Architecture of an artificial neuron.

Where,

X1, X2, X3, X4 = Rainfall inputs to ANN

W1, W2, W3, W4 = Weight to the rainfall.

O = Output of ANN.

f = Logistic sigmoidal function

The expression can be written in the mathematical form as follows and it is given by

the equation 3.5:

)),(),3(),2(),2(),3(,,()( DqttQttQtRtRtRDQSRftQ sslll …(3.5)

xxxiv

Where,

T = time of prediction, h; t1 = time period, (3hrs)

t1 = time to incorporate rainfall (in this case, t1=t-4)

R = rainfall intensity, (mh1); Q = discharge, (cumec)

SR = summation of rainfall value from t-8t to t-3ts, (mm/hr)

DQ = discharge excess between Q (t-8ts) and Q (t-3ts),(cumec).

Dq = discharge excess between Q (t-3ts) and Q (t-ts), (cumec)

Fig. 3.6 Architecture of feed forward multilayer perception (MLP).

Where,

X1, X2, X3, X4…….Xn = Rainfall inputs to ANN in input layer.

1, 2, 3, 4, 5 = Number of neurons in hidden layers.

Y = Runoff as output of neuron

3.4.3.3 Procedure for ANN model simulation

To operate ANN initially data will be arranged in one input and output i.e. observed

rainfall in one notepad sheet and observed rainfall in another notepad sheet format. The flow

chart for operation of ANN model is given in Fig. 3.7. Transfer notepad format data as input

for computing estimated output. In the ANN model epoch were set up to 1000 iteration.

Model training will be checked by using square error (MSE). When we the add input as

rainfall and output as observed runoff in neural network toolbox in MATLAB training of the

network automatically stops whenever recommended output in the form of performance plot,

training state plot, fit plot and regression plot. The output from ANN will be statistically

xxxv

tested with the observed runoff by using various statistical parameter viz. RMSE, MARE, and

correlation coefficient (R) by comparing these statistical parameters best ANN architecture

will be selected. (Mehendale G. M., 2013)

Fig. 3.7 Schematic Representation of ANN

3.5 Training procedure for neural network

In the present study network was studied in Matlab7.9 software. Where 70% data

were used for training, 15% for testing and 15% for validation purpose. The flow chart for

steps performed to operate ANN model is shown in Fig.3.7. Training of neurons is carried out

by following steps:

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1) Initially open a Matlab 7.9 software as start> Matlab 7.9 Shown in Fig 3.8:

Fig. 3.8 Opening window of Matlab 7.9 ANN toolbox

2) Type „nftool‟ in Command window > Press „Enter‟ key > the window will be display as

shown in Fig. 3.9:

Fig. 3.9 Neural network fitting tool window

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3) Click on import data tab and import input and output (target) data as shown in Fig. 3.10

and 3.11.

Fig. 3.10 Importing of data to Matlab software

Fig. 3.11 Actual importing data window

xxxviii

4) Select the percentage of data for training testing and validation as shown in Fig. 3.12.

Fig. 3.12 Dividing window into training, validation and testing

5) Select number of hidden neuron then network will be displayed as shown in Fig. 3.13.

Fig. 3.13 Selection of number of neuron window

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6) Train the network by clicking on Train tab (Fig. 3.14). It will train by using Levenberg-

Marquadt algorithm and data division is random.

Fig. 3.14 Train data by using Levenberg-Marquadt Algorithm

7) After training it will display output in the form of four plots as performance plot

regression, mean square error and training plot as shown in Fig.3.15.

Fig. 3.15 Window displays the progress of network

xl

8) Save the result by clicking on „Save Results‟ tab as shown in Fig.3.15.

Fig. 3.16 Saving the results in MATLAB 7.9 Software

9) The architecture which gives less mean square error and good regression is selected as a

network for calculating runoff. Fig. 3.17, 3.18, 3.19 and 3.20.

Fig. 3.17 Performance plot window

xli

Fig. 3.18 Training state window

Fig. 3.19 Function to fit window

xlii

Fig. 3.20 Regression plot of neural network

3.6 Statistical analysis:

The selection procedure is based on the following statistics: Correlation (COR), coefficient

of determination (R2), mean absolute relative error (MARE) and root mean square error

(RMSE). (Salunke J. R., 2013)

100

ˆ

1

n

Q

QQ

MARE

n

i i

ii

n

i

ii

n

i

ii

QQ

QQ

R

1

2

1

2

2

)(

)ˆ(

1

.

)ˆˆ()(

)ˆˆ)((

1

22

1

1

n

i

iii

n

i

i

n

i

iiii

QQQQ

QQQQ

COR

xliii

n

QQ

RMSE

n

i

ii

2

1

ˆ

Where,

n The number of data.

iQ The observed value.

iQ̂

The predicted value.

Q i The average of observed value.

Q̂ i The average of predicted value.

COR Correlation.

R2 Coefficient of Correlation.

MARE Mean absolute relative error.

RMSE Root mean square error

xliv

CHAPTER IV

RESULTS AND DISCUSSION

This chapter contains formulation or development of Artificial Neural Networks

(ANNs) for runoff prediction of previous four year (2010, 2011, 2013 and 2014) for a

Priyadarshini Watershed, College of Agricultural Engineering and Technology, Dr.

B.S.K.K.V. Dapoli (M.S.), India. The observed rainfall and observed runoff data of previous

four years (2010, 2011, 2013, and 2014) sets were used to train the model network. The

performances of models were evaluated by using various statistical parameters.

Result and discussion is presented in following sequence:

Runoff Estimation by using Rectangular weir

Runoff Estimation by using ANN Model

ANN with one input

Observed and Predicted Runoff

Statistical analysis by ANN method

4.1 Runoff Estimation by using Rectangular weir

Runoff was continuously measured at the outlet of Priyadarshini Watershed for a period of

June-October every year by using rectangular weir (RCC nala bandh) with end contractions at

both ends and it is given in Appendix-I. Monthly rainfall and runoff observed at Priyadarshini

watershed for year 2010, 2011, 2013 and 2014 are given in Table. 4.1.

Table No. 4.1 Monthly rainfall and runoff observed at Priyadarshini watershed for year

2010, 2011, 2013 and 2014.

Sr.

No.

Month Rainfall,

mm

Runoff

Measured,

ha-m

Runoff, mm Percent

runoff, mm

Year 2010

1. June, 2010 1161.4 17.58 453.90 39.50

2. July, 2010 1750 26.53 685.20 39.15

3. August, 2010 688 8.80 227.15 33.01

4. Sept, 2010 905.2 9.15 236.36 26.01

5. October, 2010 126.8 0 0 0

Total 4931.4 1602.61 34.60

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Sr.

No.

Month Rainfall,

mm

Runoff

Measured,

ha-m

Runoff, mm Percent

runoff, mm

Year 2011

1. June, 2011 918.4 3.45 89.18 9.71

2. July, 2011 2034.2 34.16 882.29 43.37

3. August, 2011 1336.1 20.05 517.82 38.75

4. Sept, 2011 524.5 4.80 124.06 23.65

5. October, 2011 115.6 0 0 0

Total 4928.8 1613.35 32.73

Year 2013

1. June, 2013 1713.2 22.28 575.52 33.59

2. July, 2013 1763.7 31.29 808.21 45.82

3. August, 2013 609.4 2.49 64.32 10.55

4. Sept, 2013 282.6 0 0 0

5. October, 2013 365.4 0 0 0

Total 4734.3 1448.05 30.58

Year 2014

1. June, 2014 346.6 0.00 0.00 0.00.

2. July, 2014 1545.7 20.42 527.32 34.11

3. August, 2014 851.9 10.40 266.59 31.29

4. Sept, 2014 595.6 7.01 182.92 30.71

5. October, 2014 22.4 0.00 0.00 0.00

Total 3362.2 976.83 29.05

4.2 Runoff Estimation by using ANN Model

In the present study, Rainfall data was tested by using logistic sigmoid function and trained

with a Levenberg-Marquardt algorithm to estimate runoff by artificial neural network. For this

purpose the neural network toolbox in Matlab 7.9 was used. All events were classified into

training, testing and validation (discussed in section 3.2). Last four years (2010, 2011, 2013

and 2014) input rainfall data and observed runoff data sets were used for operation consist of

total 198 events.

Total 70 model structures were developed with different number of neurons in the hidden

layer in each model to investigate the impact variable enabling of input dimensions on the

xlvi

model performance. Also an investigation of the best number of neurons in the hidden layer

were tested from 1 number to 70 number for example 1-1-1, 1-2-1, 1-3-1, 1-4-1 up to 1-70-1.

These represents 1 input layer, 1 output layer and 1 hidden layer with varying number of

neurons in hidden layer. The numbers of neurons in the hidden layer were increased till

reaching the maximum allowable value. This was done for each mentioned main 70 models. It

is good to mention that, the number of the neurons in the 1st (input) layer in each main model

was changing from each other but the output layer number was the same i.e. 1, therefore the

range (min, max) of the hidden layer neurons numbers was different for most of the models

depending on the input and output dimension. To estimate runoff by ANN model the each

model operation was started by dividing the input and output data into 70% for training, 15%

for validation and 15% for testing.

In this case of neural network up to 70 hidden neurons in hidden layer were studied, as after

70 hidden neurons it gives very high mean square error. The output of the program in Matlab

7.9 for artificial neural network gives mean square error and R² values directly. While other

statistical parameter RMSE were computed statistically as discussed in methodology in

section 3.6.

4.3 ANN with one input

Initially neural network was trained by using single input (rainfall) and single output (runoff)

and data was divided into 70 percent for training, 15 percent for validation and 15 percent for

testing respectively.

Total 10 best suitable architectures selected from the 70 model architecture which were

studied. The 10 best suitable architectures are 1-18-1, 1-22-1, 1-32-1, 1-34-1, 1-35-1, 1-40-1,

1-41-1, 1-45-1, 1-48-1, 1-65-1. Out of these 10 best suitable architectures, the ANN of

architecture 1-48-1 found most suitable for estimation of runoff than any other ANN

architectures (From Table 4.2).

The 1-48-1 ANN architecture gives 13.4597, 472.0640, 0.8376 and 0.9188 values for RMSE,

MAE, R² and r respectively. The results obtained from Table 4.2 and ANN of architecture 1-

48-1 found suitable for estimation of runoff. Other architectures show over estimated or under

estimated results.

xlvii

Table No. 4.2 Statistical performance of various ANN architectures.

Sr. No. ANN

architecture

RMSE MAE R² r

1. 1-1-1 26.9445 1604.7476 0.3495 0.6926

2. 1-2-1 16.5482 923.1794 0.7546 0.8693

3. 1-3-1 18.6775 529.7967 0.7071 0.8440

4. 1-4-1 16.4758 919.4499 0.7567 0.8700

5. 1-5-1 46.8732 2567.3570 -0.9089 0.7038

6. 1-6-1 17.3721 1052.4632 0.7295 0.8552

7. 1-7-1 16.5205 993.5123 0.7554 0.8692

8. 1-8-1 20.0019 812.2400 0.6415 0.8146

9. 1-9-1 34.2889 833.1672 -0.053 0.6424

10. 1-10-1 34.8407 904.5746 -0.0876 0.6307

11. 1-11-1 16.7546 842.2590 0.7484 0.8654

12. 1-12-1 62.2388 6762.6819 -2.4707 0.7985

13. 1-13-1 29.8332 743.8076 0.2025 0.7576

14. 1-14-1 17.7621 810.7652 0.7173 0.8523

15. 1-15-1 49.4704 100.7597 -1.1927 0.5740

16 1-16-1 15.5535 863.6522 0.7832 0.8864

17. 1-17-1 15.9610 794.5448 0.7717 0.8806

18. 1-18-1 14.0803 967.3900 0.8223 0.9074

19. 1-19-1 302.5474 963.3549 -81.0145 0.2704

20. 1-20-1 16.1615 931.9767 0.7659 0.8763

21. 1-21-1 18.2517 946.2198 0.7015 0.8528

22. 1-22-1 14.4823 924.7313 0.8120 0.9023

23. 1-23-1 15.6052 1033.5323 0.7818 0.8843

24. 1-24-1 27.7277 1039.0618 0.3111 0.7348

25. 1-25-1 16.3079 1048.3129 0.7617 0.8732

26. 1-26-1 17.9160 999.9701 0.7124 0.8448

27. 1-27-1 15.1982 455.4921 0.7930 0.8973

28. 1-28-1 53.8500 3224.27 -1.5989 0.4937

29. 1-29-1 29.3330 1148.5803 0.2290 0.7657

Sr. No. ANN RMSE MAE R² r

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architecture

30. 1-30-1 22.5967 718.5019 0.5424 0.8369

31. 1-31-1 17.9913 1032.1621 0.7099 0.8545

32. 1-32-1 14.5599 1026.6548 0.8100 0.9002

33. 1-33-1 23.8757 1182.6645 0.4892 0.7442

34. 1-34-1 14.1348 862.2806 0.8209 0.9066

35. 1-35-1 13.6192 1032.7056 0.8338 0.9136

36. 1-36-1 29.9699 1095.4350 0.1952 0.7453

37. 1-37-1 43.8865 771.8109 0.7256 0.5625

38. 1-38-1 17.9428 1358.4755 0.7115 0.8637

39. 1-39-1 17.5525 1193.5605 0.7239 0.8631

40. 1-40-1 14.0307 590.2260 0.8236 0.9078

41. 1-41-1 14.6514 799.4085 0.8076 0.9002

42. 1-42-1 20.0217 624.0466 0.6408 0.8169

43. 1-43-1 20.1264 1007.5110 0.6338 0.7978

44. 1-44-1 16.4001 903.6658 0.7590 0.8729

45. 1-45-1 14.4142 761.4747 0.8138 0.9021

46. 1-46-1 117.1673 700.3050 -11.3003 0.3124

47. 1-47-1 91.8162 998.6626 -6.6533 -0.0865

48. 1-48-1 13.4597 472.0690 0.8376 0.9188

49. 1-49-1 18.6037 883.9618 0.6898 0.8481

50. 1-50-1 34.0808 530.9210 -0.0406 0.6960

51. 1-51-1 17.6001 948.7819 0.7224 0.8565

52. 1-52-1 37.9051 313.8737 0.2873 0.6387

53. 1-53-1 20.5796 1096.9607 0.6205 0.8401

54. 1-54-1 23.4060 1116.1307 0.5091 0.7285

55. 1-55-1 26.7320 778.9217 0.3597 0.7874

56. 1-56-1 16.1660 786.2770 0.7658 0.8767

57. 1-57-1 19.8354 708.0767 0.6474 0.8277

58. 1-58-1 20.2809 543.2302 0.6314 0.8060

59. 1-59-1 21.5734 736.4091 0.5829 0.7733

Sr. No. ANN

architecture

RMSE MAE R² r

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60. 1-60-1 16.9394 931.1626 0.7428 0.8623

61. 1-61-1 31.0347 1059.2329 0.1370 0.6994

62. 1-62-1 28.9921 854.9661 0.2468 0.7308

63. 1-63-1 72.2442 836.9484 -3.6763 0.3991

64. 1-64-1 25.5113 498.9529 0.4168 0.7364

65. 1-65-1 14.2817 1103.3821 0.8172 0.9057

66. 1-66-1 19.5598 839.1704 0.6572 0.8253

67. 1-67-1 19.3489 892.2280 0.6645 0.8491

68. 1-68-1 21.2489 868.5564 0.5954 0.8158

69. 1-69-1 36.0408 1025.6384 0.1638 0.6425

70. 1-70-1 31.8059 1025.3394 0.093 0.6870

4.3.1 Most suitable ANN Architectures:

Total 10 best suitable architectures selected from the 70 model architecture based on the

statistical parameter and they are given in Table 4.3.

Table No. 4.3 Most suitable ANN architectures based on statistical performance.

Sr. No. ANN

architecture

RMSE MAE R² r

1. 1-18-1 14.0803 967.3900 0.8223 0.9074

2. 1-22-1 14.4823 924.7313 0.8120 0.9023

3. 1-32-1 14.5599 1026.6548 0.8100 0.9002

4. 1-34-1 14.1348 862.2806 0.8209 0.9066

5. 1-35-1 13.6192 1032.7056 0.8338 0.9136

6. 1-40-1 14.0307 590.2260 0.8236 0.9078

7. 1-41-1 14.6514 799.4085 0.8076 0.9002

8. 1-45-1 14.4142 761.4747 0.8138 0.9021

9. 1-48-1 13.4597 472.0690 0.8376 0.9188

10. 1-65-1 14.2817 1103.3821 0.8172 0.9057

l

These 10 best suitable architectures represented graphically are as follows:

1) Architecture 1-18-1:

The 1-18-1 ANN architecture gives 14.0803, 967.39, 0.8223 and 0.9074 values for

RMSE, MAE, R² and r respectively. The curve has been plotted for the observed runoff and

predicted runoff of architecture 1-18-1 and it is shown in Fig. 4.1.

As shown in Fig 4.2 the number of scatter points above the average line are more in

number hence the result shows that runoff has been overestimated.

Fig. 4.1: Hydrograph of Date versus observed runoff and predicted runoff for 1-18-1

architecture.

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Linear (Predicted Runoff)

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Fig. 4.2: Scatter plot of observed runoff versus predicted runoff for 1-18-1 architecture.

2) Architecture 1-22-1







architecture.


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As shown in Fig 4.6 the number of scatter points below the average line are more in

number hence the result shows that runoff has been underestimated.


architecture.


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architecture.


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4.4 Observed and Predicted Runoff:

Last four years (2010, 2011, 2013 and 2014) input observed rainfall data and observed

runoff data sets were used to train the model network. Total 10 best suitable architectures

selected from the 70 model architecture. The 10 best suitable architectures are1-18-1, 1-22-1,

1-32-1, 1-34-1, 1-35-1, 1-40-1, 1-41-1, 1-45-1, 1-48-1, 1-65-1. Out of this 10 model

architectures, ANN model the neural network with 1-48-1 architecture gives better result than

other architectures under study. The observed runoff and predicted runoff of architecture 1-

48-1 is shown in Table 4.4 and the curve has been plotted and it is shown in Fig. 4.17.



Table No. 4.4 Observed Runoff and Predicted Runoff for 1-48-1 Architecture.

Sr. No. Date Observed Runoff (mm) Predicted Runoff (mm)

1 16-06-2010 21.7 54.26

2 17-06-2010 99.32 110.01

3 18-06-2010 24.17 16.91

4 19-06-2010 185.91 185.91

5 20-06-2010 77.83 3.05

6 21-06-2010 21.2 16.88

7 22-06-2010 11.61 11.25

8 23-06-2010 9.86 15.09

9 24-06-2010 2.3 3.41

10 18-07-2010 6.13 17.79

11 19-07-2010 5.17 15.99

12 20-07-2010 19.66 42.71

13 21-07-2010 54.03 69.69

14 22-07-2010 124.38 109.91

15 23-07-2010 66.71 54.07

16 24-07-2010 31.95 26.90

17 25-07-2010 82.98 76.62

18 26-07-2010 47.45 59.07

19 27-07-2010 68 59.40

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20 28-07-2010 31.72 22.07


21 29-07-2010 38.41 28.09

22 30-07-2010 40.6 18.56

23 31-07-2010 68 51.81

24 01-08-2010 62.35 42.05

25 02-08-2010 16.28 10.99

26 03-08-2010 29.49 24.86

27 04-08-2010 2.87 3.99

28 05-08-2010 0.33 1.92

29 06-08-2010 0.07 1.92

30 11-08-2010 0.09 4.80

31 12-08-2010 10.78 11.97

32 18-08-2010 2.4 12.92

33 19-08-2010 10.45 8.01

34 20-08-2010 5.17 10.99

35 24-08-2010 8.56 19.61

36 25-08-2010 18.23 19.59

37 26-08-2010 19.18 20.56

38 27-08-2010 4.9 2.96

39 28-08-2010 6.79 4.84

40 29-08-2010 17.76 9.89

41 30-08-2010 2.1 2.42

42 31-08-2010 3.56 3.56

43 01-09-2010 36.22 36.47

44 02-09-2010 32.28 26.53

45 03-09-2010 5.8 4.28

46 04-09-2010 1.04 2.63

47 05-09-2010 11.8 17.74

48 06-09-2010 40.98 51.22

49 07-09-2010 31.15 12.87

50 08-09-2010 4.15 5.84

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51 09-09-2010 62.15 73.01


52 11-09-2010 7.4 2.30

53 12-09-2010 2.76 4.19

54 13-09-2010 0.62 1.95

55 17-06-2011 21.68 27.14

56 18-06-2011 4.88 4.39

57 09-07-2011 8.95 37.91

58 12-07-2011 3.99 11.39

59 13-07-2011 14.64 24.98

60 14-07-2011 57.61 50.84

61 15-07-2011 117.78 110.01

62 16-07-2011 35.86 12.38

63 17-07-2011 67.5 110.00

64 18-07-2011 124.78 121.66

65 19-07-2011 87.94 58.51

66 20-07-2011 19.72 5.56

67 22-07-2011 13.35 15.99

68 23-07-2011 15.08 9.07

69 24-07-2011 5.19 4.23

70 25-07-2011 4.58 7.12

71 26-07-2011 10.88 26.44

72 27-07-2011 8.22 9.41

73 28-07-2011 8.95 10.39

74 29-07-2011 76.36 99.02

75 30-07-2011 97.57 109.94

76 31-07-2011 103.34 112.24

77 01-08-2011 110.9 109.99

78 02-08-2011 33.53 23.13

79 03-08-2011 15.08 25.01

80 04-08-2011 18.29 52.50

81 05-08-2011 2.66 2.26

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82 06-08-2011 1.51 1.93


83 07-08-2011 1.73 12.38

84 08-08-2011 1.31 6.47

85 28-08-2011 94.32 143.11

86 29-08-2011 166.49 166.49

87 30-08-2011 55.56 50.47

88 31-08-2011 16.43 9.85

89 01-09-2011 2.17 5.56

90 02-09-2011 69.68 68.30

91 03-09-2011 39.45 24.85

92 04-09-2011 10.48 26.44

93 05-09-2011 1.73 2.01

94 06-09-2011 0.22 2.46

95 07-09-2011 0.33 6.97

96 11-06-2013 34.92 34.85

97 12-06-2013 18.6 16.91

98 14-06-2013 104.28 51.72

99 15-06-2013 60.75 67.54

100 16-06-2013 96.52 96.53

101 17-06-2013 74.23 68.40

102 18-06-2013 53.58 28.16

103 19-06-2013 42.62 62.64

104 20-06-2013 4.53 1.99

105 21-06-2013 15.63 14.31

106 22-06-2013 2.55 3.83

107 23-06-2013 2.08 4.34

108 24-06-2013 1 8.25

109 25-06-2013 25.94 19.99

110 26-06-2013 17.58 10.38

111 27-06-2013 13.93 6.15

112 28-06-2013 1.66 2.96

lxiv

113 29-06-2013 2.23 3.89


114 30-06-2013 1.09 12.02

115 01-07-2013 11.49 16.91

116 02-07-2013 9.17 27.59

117 03-07-2013 37.06 56.93

118 05-07-2013 8.21 3.83

119 07-07-2013 39.96 9.90

120 08-07-2013 18.41 29.15

121 09-07-2013 28.35 51.83

122 10-07-2013 59.8 29.16

123 11-07-2013 73.24 52.45

124 12-07-2013 82.95 51.78

125 13-07-2013 41.56 34.02

126 14-07-2013 12.75 10.36

127 15-07-2013 8.46 6.80

128 16-07-2013 6.6 3.23

129 17-07-2013 11.63 28.30

130 18-07-2013 32.34 19.29

131 19-07-2013 14.02 13.18

132 20-07-2013 42.8 61.75

133 21-07-2013 37.81 51.83

134 22-07-2013 27.9 56.93

135 23-07-2013 18.28 18.56

136 24-07-2013 100.96 100.99

137 25-07-2013 55.04 26.06

138 26-07-2013 16.96 7.12

139 27-07-2013 7.3 8.97

140 28-07-2013 5.17 7.73

141 25-07-2013 14.2 26.53

142 26-07-2013 36.77 51.78

143 27-07-2013 10.97 13.84

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144 28-07-2013 2.38 1.99


145 13-07-2014 19.8 24.89

146 14-07-2014 36.75 38.77

147 15-07-2014 68.95 49.88

148 16-07-2014 96.9 98.50

149 17-07-2014 42.42 39.12

150 18-07-2014 15.52 6.80

151 19-07-2014 5.5 10.87

152 20-07-2014 3.4 8.25

153 21-07-2014 2.1 4.51

154 22-07-2014 26.36 13.83

155 23-07-2014 10.42 8.13

156 24-07-2014 26.68 50.83

157 25-07-2014 9.52 4.28

158 26-07-2014 0.33 3.23

159 27-07-2014 1 3.41

160 28-07-2014 54.09 56.80

161 29-07-2014 27.49 21.39

162 30-07-2014 15.23 13.62

163 31-07-2014 64.86 61.03

164 01-08-2014 55.36 68.08

165 02-08-2014 12.9 2.70

166 03-08-2014 3.35 3.49

167 04-08-2014 14.79 9.36

168 05-08-2014 45.91 44.83

169 06-08-2014 15.38 7.12

170 07-08-2014 6.53 9.82

171 08-08-2014 2.87 3.83

172 09-08-2014 0.05 2.55

173 10-08-2014 4.73 10.11

174 11-08-2014 3.62 4.72

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175 12-08-2014 5.98 14.29


176 13-08-2014 1.98 3.97

177 14-08-2014 0.1 2.26

178 15-08-2014 0.03 2.36

179 26-08-2014 0.42 1.94

180 27-08-2014 5.77 9.62

181 28-08-2014 5.5 9.90

182 29-08-2014 0.15 1.95

183 30-08-2014 13.07 1.99

184 31-08-2014 68.1 68.10

185 01-09-2014 45.04 51.93

186 02-09-2014 8.46 3.45

187 03-09-2014 9.27 3.99

188 04-09-2014 13.7 10.58

189 05-09-2014 10.29 23.21

190 06-09-2014 83.18 83.27

191 07-09-2014 4.9 1.93

192 08-09-2014 1.58 1.98

193 09-09-2014 3.48 14.31

194 10-09-2014 0.61 4.23

195 11-09-2014 0.02 10.39

196 12-09-2014 1.58 3.32

197 13-09-2014 0.77 3.71

198 14-09-2014 0.04 1.94

4.5 Statistical analysis by ANN method

After several trials a three layer ANN architecture consisting of one input layer, one

hidden layer and one output layer was found best for data set and for estimation of runoff. The

number of neurons in one input and output layer is up to 70. This resulted 1-48-1 as best

model configuration and indicated that 1 neuron in hidden layer fitted best on test data and

shows a high degree of accuracy with training data set. ANN with above configuration was

lxvii

trained several iterations and best result were obtained with 13 iterations on the basis of

minimum percent mean square error (PMSE).

lxviii

CHAPTER V

SUMMARY AND CONCLUSIONS

The runoff forecasting is very essential for planning and management of water

resources. The artificial neural network is the most efficient runoff forecasting method.

Artificial Neural Networks (ANNs) are non-linear mapping structures based on the functions

of human brain. ANNs can identify and learn correlated patterns between input data sets and

corresponding target values. The accurate runoff prediction is one of the greatest challenges in

hydrology. Artificial Neural Networks have been proven to be the most successful tool in

dealing with highly complicated problems due to their powerful capability to model non-

linear systems without the need to make any assumptions.

5.1 Summary:

The present study was undertaken with the specific objectives as, to develop rainfall-

runoff model using Artificial Neural Network for a Priyadarshini watershed. Rainfall runoff

relationship was studied by Artificial Neural Network (ANN) using Matlab 7.9 software.

Total 70 numbers of ANN architectures were used for the computation of runoff. Total 10

best suitable architectures selected from the 70 model architecture. The 10 best suitable

architectures are 1-18-1, 1-22-1, 1-32-1, 1-34-1, 1-35-1, 1-40-1, 1-41-1, 1-45-1, 1-48-1, 1-65-

1. Out of these 10 best suitable architectures, the ANN of architecture 1-48-1 found most

suitable for estimation of runoff than any other ANN architectures. ANN with 1-48-1

architecture is found to be better which gives 13.4597, 472.0640, 0.8376 and 0.9188 values

for Root Mean Square Error, Mean Absolute Error, Correlation (r) and Coefficient of

Determination (R²) respectively.

The Artificial Neural Network (ANN) models show an appropriate capability to model

hydrological process. They are useful and powerful tools to handle complex problems

compared with other traditional models. In this study, the results show clearly that the

artificial neural networks are capable of model rainfall runoff relationship in the Priyadarshini

watershed in which the general enhancement achieved by using neural networks in many

other hydrological fields.

The present study also provides a valuable data based on continuous rainfall and

runoff in Priyadarshini Watershed for the year 2010, 2011, 2013 and 2014. Runoff was

continuously measured at the outlet of Priyadarshini Watershed for a period of June-October

2010, 2011, 2013 and 2014 using rectangular weir (RCC nala bandh) with end contractions at

both ends. These data help to understand the complex physical processes in the watershed.

lxix

In the monsoon period of the year 2010 from June to October total rainfall occurred

was 4631.4 mm contributing 1161.4 mm in the month of June followed by 1750 mm,

688 mm, 905.2 mm and 126.8 mm in the month of July, August, September and

October respectively. In the month of June runoff depth was 453.90 mm, 685.20 mm

in July, 227.15 mm in August and 236.36 mm in September were observed. The total

runoff depth was 1602.61 mm and the total runoff found to be 34.60 per cent for the

year 2010.


was 4928.8 mm contributing 918.4 mm in the month of June followed by 2034.2 mm,

1336.1 mm, 524.5 mm and 115.6 mm in the month of July, August, September and

October respectively. In the month of June runoff depth was 89.18 mm, 882.29 mm in

July, 517.82 mm in August and 124.06 mm in September were observed. The total

runoff depth was 1613.35 mm and the total runoff found to be 32.73 per cent for the

year 2011.


was 4734.3 mm contributing 1713.2 mm in the month of June followed by 1763.7

mm, 609.4 mm, 282.6 mm and 365.4 mm in the month of July, August, September

and October respectively. In the month of June runoff depth was 575.52 mm, 808.21

mm in July and 64.32 mm in August were observed. The total runoff depth was

1448.05 mm and the total runoff found to be 30.58 per cent for the year 2013.


was 3362.2 mm contributing 346.6 mm in the month of June followed by 1545.7 mm,

851.9 mm, 595.6 mm and 22.4 mm in the month of July, August, September and

October respectively. In the month of July runoff depth was 527.32 mm, 266.59 mm

in August and 182.92 mm in September were observed. The total runoff depth was

976.83 mm and the total runoff found to be 29.05 per cent for the year 2014.

5.2 Conclusions:

Following conclusion is drawn from above results.

1) Total 70 model structures were developed, each one with different number of neurons

in the hidden layer to investigate the probability impacts of enabling rainfall-runoff.

2) Out of 70 model structure, 10 best suitable models were selected based on statistical

performance. Out of 10 ANN model, the 1-48-1 as best model configuration and

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indicated that 48 neuron in hidden layer fitted best on test data and shows high degree

of accuracy with training data set than other ANN architectures.

3) The performance of ANN 1-48-1 architecture in estimation of runoff from rainfall data

was checked statistically, Hence, this ANN 1-48-1 architectures can be adopted to

estimate runoff from ungauged watershed with rainfall as input.

4) ANN model with 1-48-1 architecture is found to be better which gives 13.4597,

472.0640, 0.8376 and 0.9188 values for Root Mean Square Error, Mean Absolute

Error, Correlation (r) and Coefficient of Determination (R²) respectively.

5) The proposed approach can be a very efficient tool and useful alternative for the

computation of rainfall-runoff relationship.

lxxi

CHAPTER VI

BIBLIOGRAPHY

Anonymous, (2000a). ASCE Task Committee on Application of the Artificial Neural

Networks in Hydrology. Artificial Neural Network in Hydrology I; Preliminary

Concepts. Journal of Hydrologic Engineering, 5(2): 115-123.

Anonymous, (2000b). ASCE Task Committee on Application of the Artificial Neural

Networks in Hydrology, 2000b. Artificial Neural Network in Hydrology II; Preliminary

Concepts. Journal of Hydrologic Engineering, 5(2): 124-137.

Arslan, C. A. 2011. Rainfall-Runoff Modelling based on Artificial Neural Network. European

Journal of Scientific Research. 65: 490-506.

Avinash, A., S. K. Mishra, S. Ram and J. K. Singh. 2006. Simulation of Runoff and Sediment

Yield using Artificial Neural Networks. Biosystems Engineering. 94 (4): 597–613.

Bansal, R. K. 2010. A textbook of Fluid Mechanics and Hydraulic Machines. Chapter 8:

Notches and Weir. pp: 377-379.

Chen, J. and B. Adams. 2006. "Semi distributed Form of the Tank Model Coupled with

Artificial Neural Networks." Journal of Hydrologic Engineering. 11 (5): 408-417.

Dawson, C. W. and R. L. Wilby. 1998. An Artificial Neural Network Approach to Rainfall-

Runoff Modelling. Journal of Hydrological Sciences. 43: 47-66.

El-shafie, A., M. Mukhlisin, A. A. Najah and M. R. Taha. 2011. Performance of Artificial

Neural Network and Regression Techniques for Rainfall-Runoff Prediction.

International Journal of the Physical Sciences. 6 (8): 1997-2003.

Jain, A. and S. Srinivasulu. 2006. Integrated approach to Model Decomposed Flow

Hydroghaph using Artificial Neural Network and Conceptual Techniques. Journal of

Hydrology. 317: 291-306.

Junsawang, P., J. Asavanant and C. Lursinsap. 2007. Artificial Neural Network Model for

Rainfall-Runoff Relationship. Advanced Virtual and Intelligent Computing Center

(AVIC). Department of Mathematics, Faculty of Science, Chulalongkorn University,

Bangkok, 10330, Thailand.

Joshi, J. and V. M. Patel. 2011., "Rainfall-Runoff Modeling Using Artificial Neural

Network", National Conference on Recent Trends in Engineering & Technology.

http://ascelibrary.org/action/doSearch?action=runSearch&type=advanced&result=true&prevSearch=%2Bauthorsfield%3A%28Chen%2C+J%29

http://ascelibrary.org/action/doSearch?action=runSearch&type=advanced&result=true&prevSearch=%2Bauthorsfield%3A%28Adams%2C+B+J%29

lxxii

Kaltech, M. A. 2008. "Rainfall-Runoff Modeling Using Artificial Neural Networks (Ann's):

Modelling and Understanding". Caspian Journal of Environmental Science. 6 (1): 53-

58.

Mehendale, G. M. 2013. Study of Rainfall Runoff Relationship using SCS-CN and ANN

Models for Bench Terraces, Dapoli. Unpublished M. Tech Thesis submitted to Dr.

B.S.K.K.V., Dapoli.

Minns, A. W. and M. J. Hall. 1996. Artificial Neural Networks as Rainfall-Runoff Models.

Hydrological Sciences. 41: 399–417.

Maier, H. R. and G. C. Dandy. 2000. "Neural Networks for Prediction and Forecasting of

Water Resources Variables: A review of Modeling Issues and Applications"

Environmental Modeling and Software. 15: 101-123.

Nandgude, S., J. Salunkhe, S. Shinde, D. Mahale and V. Shinde. 2014. Rainfall- Runoff

modeling of Small Watershed in Konkan Region Using Artificial Neural Network.

Trends in Biosciences. 7(6): 480-485.

Rajurkar, M. P. 2004. Modeling of the Daily Rainfall-Runoff Relationship with Artificial

Neural Network. Journal of Hydrology. 285: 96-113.

Sajikumar, N. and B. S. Thandaveswara. 1999. A Non-Linear Rainfall-Runoff Model using an

Artificial Neural Network. Journal of Hydrology. 216: 32-55.

Salunke, J. R. 2013. Rainfall- Runoff Modelling of Priyadarshini Watershed using Artificial

Neural Network. Unpublished B. Tech Thesis submitted to Dr. B.S.K.K.V., Dapoli.

Solaimani K. 2009. "Rainfall-Runoff Prediction Based On Artificial Neural Network; A case

study Jarahi watershed". American-Eurasian Journal of Agricultural & Environmental

Science. 5(6): 856- 865.

Sarkar, A. and R. Kumar. 2012. Artificial Neural Networks for Event Based Rainfall-Runoff

Modeling. Journal of Water Resource and Protection. 4: 891-897.

Sudheer, K. P. and A. Jain. 2002. The Internal Behavior of Artificial Neural Network River

Flow Models. Hydrological Processes. 8: 833-844.

Singh, P. V., Akhilesh Kumar, J. S. Rawat and Devendra Kumar. 2013. Artificial Neural

Networks Based Daily Rainfall-Runoff Model for an Agricultural Hilly Watershed.

International Journal of Engineering and Management Science. 4 (2): 108-112.

lxxiii

Riad, S. and J. Mania. 2004. Rainfall-Runoff Model Using an Artificial Neural Network

Approach. Mathematical and Computer Modelling. 4: 839-846.

Rumelhart, D. and J. McClelland. 1986. Parallel Distributed Processing. MIT Press,

Cambridge.

Yazdani, M. R., B. Saghafian, M. H. Mahdian, and S. Soltani. 2009. Monthly Runoff

Estimation using Artificial Neural Network. Journal of Agriculture Science Technology.

11: 355-362.

Zeland, C. M., D. H. Burn and S. P. Simonovic. 1999. Short Term Stream Flow Forecasting

using Artificial Neural Network. Journal of Hydrology. 214: 32-48.

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VII. APPENDICES

APPENDIX I

Table No. 7.1 Four years rainfall- runoff data of Priyadarshini watershed.

Sr. No. Date Observed

Rainfall (mm)

Observed

Runoff (mm) 1 16-06-2010 134 21.7

2 17-06-2010 193.8 99.32

3 18-06-2010 33 24.17

4 19-06-2010 413 185.91

5 20-06-2010 9.6 77.83

6 21-06-2010 50.4 21.2

7 22-06-2010 27 11.61

8 23-06-2010 47 9.86

9 24-06-2010 10.4 2.3

10 18-07-2010 43 6.13

11 19-07-2010 35.2 5.17

12 20-07-2010 100.4 19.66

13 21-07-2010 137.6 54.03

14 22-07-2010 181.4 124.38

15 23-07-2010 87.8 66.71

16 24-07-2010 54 31.95

17 25-07-2010 160 82.98

18 26-07-2010 108.8 47.45

19 27-07-2010 85.4 68.00

20 28-07-2010 45.6 31.72

21 29-07-2010 51.8 38.41

22 30-07-2010 58.6 40.60

23 31-07-2010 155.6 68.00

24 01-08-2010 80.2 62.35

25 02-08-2010 26.6 16.28

26 03-08-2010 51.4 29.49

27 04-08-2010 12.2 2.87

28 05-08-2010 1.2 0.33

29 06-08-2010 0.8 0.07

30 11-08-2010 15 0.09

31 12-08-2010 49.6 10.78

32 18-08-2010 42 2.40

33 19-08-2010 38 10.45

34 20-08-2010 26.6 5.17

35 24-08-2010 46.2 8.56

36 25-08-2010 43.4 18.23

37 26-08-2010 46 19.18

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38 27-08-2010 9.4 4.90


Rainfall (mm)

Observed

Runoff (mm) 39 28-08-2010 15.1 6.79

40 29-08-2010 48.2 17.76

41 30-08-2010 8 2.10

42 31-08-2010 10.8 3.56

43 01-09-2010 99.6 36.22

44 02-09-2010 61.6 32.28

45 03-09-2010 13.4 5.80

46 04-09-2010 8.6 1.04

47 05-09-2010 54.6 11.80

48 06-09-2010 104.4 40.98

49 07-09-2010 47.4 31.15

50 08-09-2010 16.8 4.15

51 09-09-2010 159.6 62.15

52 11-09-2010 7.6 7.40

53 12-09-2010 13 2.76

54 13-09-2010 4.2 0.62

55 17-06-2011 150.4 21.68

56 18-06-2011 13.8 4.88

57 09-07-2011 79.2 8.95

58 12-07-2011 27.2 3.99

59 13-07-2011 71.8 14.64

60 14-07-2011 104 57.61

61 15-07-2011 194.2 117.78

62 16-07-2011 28.4 35.86

63 17-07-2011 189 67.50

64 18-07-2011 141.8 124.78

65 19-07-2011 108.6 87.94

66 20-07-2011 16.4 19.72

67 22-07-2011 35.2 13.35

68 23-07-2011 55.6 15.08

69 24-07-2011 13.2 5.19

70 25-07-2011 18.4 4.58

71 26-07-2011 75 10.88

72 27-07-2011 41.2 8.22

73 28-07-2011 56.6 8.95

74 29-07-2011 163 76.36

75 30-07-2011 182 97.57

76 31-07-2011 141 103.34

77 01-08-2011 184.6 110.90

78 02-08-2011 45 33.53

79 03-08-2011 73.4 15.08

80 04-08-2011 82.6 18.29

81 05-08-2011 7.4 2.66

82 06-08-2011 2.4 1.51

83 07-08-2011 28.4 1.73

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84 08-08-2011 17.6 1.31


Rainfall (mm)

Observed

Runoff (mm) 85 28-08-2011 222 94.32

86 29-08-2011 225 166.49

87 30-08-2011 103.6 55.56

88 31-08-2011 24.1 16.43

89 01-09-2011 16.4 2.17

90 02-09-2011 148.8 69.68

91 03-09-2011 72.4 39.45

92 04-09-2011 75 10.48

93 05-09-2011 5.5 1.73

94 06-09-2011 8.15 0.22

95 07-09-2011 38.4 0.33

96 11-06-2013 208 34.92

97 12-06-2013 33 18.60

98 14-06-2013 128 104.28

99 15-06-2013 116 60.75

100 16-06-2013 218.2 96.52

101 17-06-2013 112.4 74.23

102 18-06-2013 76 53.58

103 19-06-2013 110 42.62

104 20-06-2013 5.2 4.53

105 21-06-2013 36 15.63

106 22-06-2013 11.6 2.55

107 23-06-2013 13.6 2.08

108 24-06-2013 20 1.00

109 25-06-2013 59 25.94

110 26-06-2013 48 17.58

111 27-06-2013 40 13.93

112 28-06-2013 9.4 1.66

113 29-06-2013 11.8 2.23

114 30-06-2013 28 1.09

115 01-07-2013 33 11.49

116 02-07-2013 68.2 9.17

117 03-07-2013 87 37.06

118 05-07-2013 11.6 8.21

119 07-07-2013 49 39.96

120 08-07-2013 65.2 18.41

121 09-07-2013 105 28.35

122 10-07-2013 65 59.80

123 11-07-2013 88.2 73.24

124 12-07-2013 129 82.95

125 13-07-2013 53 41.56

126 14-07-2013 25.4 12.75

127 15-07-2013 18 8.46

128 16-07-2013 10 6.60

129 17-07-2013 63 11.63

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130 18-07-2013 58.8 32.34


Rainfall (mm)

Observed

Runoff (mm) 131 19-07-2013 29.2 14.02

132 20-07-2013 158 42.80

133 21-07-2013 105 37.81

134 22-07-2013 87 27.90

135 23-07-2013 46.4 18.28

136 24-07-2013 170 100.96

137 25-07-2013 70 55.04

138 26-07-2013 18.4 16.96

139 27-07-2013 21.5 7.30

140 28-07-2013 19.2 5.17

141 01-08-2013 61.6 14.20

142 02-08-2013 125 36.77

143 03-08-2013 29.8 10.97

144 04-08-2013 5.2 2.38

145 13-07-2014 73 19.8

146 14-07-2014 92.6 36.75

147 15-07-2014 103 68.95

148 16-07-2014 140 96.9

149 17-07-2014 79.5 42.42

150 18-07-2014 18 15.52

151 19-07-2014 26.4 5.5

152 20-07-2014 20 3.4

153 21-07-2014 14.2 2.1

154 22-07-2014 97.6 26.36

155 23-07-2014 19.8 10.42

156 24-07-2014 82.2 26.68

157 25-07-2014 13.4 9.52

158 26-07-2014 10 0.33

159 27-07-2014 10.4 1

160 28-07-2014 119.6 54.09

161 29-07-2014 45.8 27.49

162 30-07-2014 29.6 15.23

163 31-07-2014 118.2 64.86

164 01-08-2014 112.2 55.36

165 02-08-2014 8.8 12.9

166 03-08-2014 10.6 3.35

167 04-08-2014 22.6 14.79

168 05-08-2014 100.8 45.91

169 06-08-2014 18.4 15.38

170 07-08-2014 24 6.53

171 08-08-2014 11.6 2.87

172 09-08-2014 8.4 0.05

173 10-08-2014 24.8 4.73

174 11-08-2014 14.8 3.62

175 12-08-2014 30.2 5.98

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176 13-08-2014 12.1 1.98


Rainfall (mm)

Observed

Runoff (mm) 177 14-08-2014 7.4 0.1

178 15-08-2014 7.8 0.03

179 26-08-2014 3.4 0.42

180 27-08-2014 23.4 5.77

181 28-08-2014 37.4 5.5

182 29-08-2014 4 0.15

183 30-08-2014 5.2 13.07

184 31-08-2014 335.2 68.1

185 01-09-2014 130.2 45.04

186 02-09-2014 10.5 8.46

187 03-09-2014 12.2 9.27

188 04-09-2014 37.2 13.7

189 05-09-2014 51.2 10.29

190 06-09-2014 206.7 83.18

191 07-09-2014 3 4.9

192 08-09-2014 5 1.58

193 09-09-2014 36 3.48

194 10-09-2014 13.2 0.61

195 11-09-2014 56.6 0.02

196 12-09-2014 10.2 1.58

197 13-09-2014 11.2 0.77

198 14-09-2014 3.6 0.04

development of rainfall runoff model using …...priyadarshini watershed”, submitted to faculty of...

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