yapay sİnİr aĞi İle hava sicaklik tahmİnİ · 21.5.2014 4 •weather temperature was estimated...

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AN APPLICATION OF NEURAL NETWORKS FOR WEATHER

TEMPERATURE FORECASTING

EE-588 ADVANCED TOPICS IN NEURAL NETWORK

2013911131 AYŞE SOYLU

Plan

• Introduction

• Literature Review

• The Neural Network Model

– Feed Forward Back Propagation Learning Algorithm

• Weather Temperature Forecasting with Artifical Neural Network(ANN)

• Results And Discussion

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1. INTRODUCTION

• Weather is a continuous, data-intensive, multidimensional, dynamic and chaotic process and these properties make weather prediction a big challenge.

• Weather forecasting has become an important field of research in the last few decades.

• Generally, two methods are used for weather forecasting (a) the empirical approach and (b) the dynamical approach.


• The first approach is based on the occurrence of analogs and is often referred by meteorologists as analog forecasting. This approach is useful for predicting local-scale weather if recorded datas are plentiful.

• The second approach is based on equations and forward simulations of the atmosphere and is often referred to as computer modeling. The dynamical approach is only useful for modeling large-scale weather phenomena and may not forecast short-term weather efficiently. (Devi, 2012)


• Artificial Neural Networks(ANN) techniques are applied to various fields such as classification, optimization, forecasting, recognition, modeling and learning. ANNs provide a methodology for solving many types of nonlinear problems that are difficult to solve by traditional techniques. (Zurada, 1992).

• Thus, these properties of ANNs are well suited to the problem of weather forecasting under consideration.


• This study will be based on develop the most suitable ANN method and its associated training technique for weather prediction. Performance quantification of the developed model will be comparison of the regression models based on a number of statistical measures.


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2. LITERATURE REVIEW • The idea of numerical weather

forecasting-predicting the weather by solving mathematical equations was formulated in 1904 by Vilhelm Bjerknes (1862-1951, Norwegian) and developed by British mathematician Lewis Fry Richardson (1881-1953, British).


• In the late 1940s, using one of the earliest modern computers, Mathematician John volt Neumann (1903-1957, Hungarian -American) and scientist Jule Charney (1917-1981, American) to apply the computer to weather forecasting.

• In 1950s, numerical forecasts were being made on a regular basis. Modern technology, particularly computers and weather satellites and the availability of data provided by coordinated meteorological observing Networks, has resulted in enormous improvements in the accuacy of weather forecasting.


Historical background of ANN

• 1943 McCulloch and Pitts proposed the first computational models of neuron.

• 1949 Hebb proposed the first learning rule.

• 1958 Rosenblatt’s work in perceptrons.

• 1969 Minsky and Papert’s exposed limitation of the theory.

• 1970s Decade of dormancy for neural networks.

• 1980-90s Neural network return (self-organization, back-propagation algorithms, etc)


• Most scientist such as Kaur(2011), Maqsood(2004), Mathur(2007), Baboo(2010), Caltagirone (2011) and Abhishek(2012) different artificial neural networks (ANN) models have been used in weather prediction.

• For example; Multilayer Perceptron Networks (MLP), Elman Recurrent Neural Network (ERNN), Radial Basis Function Network (RBFN) and the Hopfield Model (HFM)


3. THE ARTIFICAL NEURAL NETWORK

• An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information


Aktivation Function

Unit Step

Sigmoid

Piecewise Linear


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Aktivation Function

Gaussian

Identity

Hiberbolik f(s) = (1 – exp(-2s))/

Tanjant (1 +exp(-2s))

f (x) = x


The ANN Model

Structure : FeedForward

Learning Method : Supervised

Learning Algorithm: Hebb

Apropriate for linear problems (And ,Or etc)


Multi-Layer Perceptron

Structure: FeedForward

Learning Method : Supervised

Learning Algorithm: Delta Learning Rule /BackPropagation

Apropriate for Linear or nonlinear problem


Feed Forward Back Propagation

Learning Algorithm

The structure of backpropagation algoritması of MLP 21.5.2014 Ayşe SOYLU


Weather Temperature Forecasting with ANN


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• Weather temperature was estimated for Adana city in the application part. In forecasting of valuables, the daily datas recorded between 2000 and 2013 years were received from Turkish State Meteorological Service.

• While the datas of 2000-2010 years were training data, the datas of between 2011-2013 were used for testing data.


Meteorological Data


• The model has been normalized to analyze on Matlab software.

• ANN architecture designed for system.



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Results




To compare estimated values with the actual values, this values export to Excel.


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• Comparison of estimated values with the actual values


REFERENCES

• Abhishek K., Singh M.P., Ghosh S., Anand A.,(2012), “Weather forecasting model using Artificial Neural Network”, SciVerse Science Direct, Procedia Technology 4 311 – 318

• Baboo S., Shereef I.K., (2010), “An e_cient weather forecasting system using Artificial neural network”,

International Journal of Environmental Science and Development, 1(4):321-326 • Caltagirone S. (2011), “Air temperature prediction using evolutionary arti_cial neural Networks”, Master's

thesis, University of Portland College Of Engineering, 5000 N. Willamette Blvd. Portland

• Devi C.J.,Reddy B.S.P, Kumar K.V., Reddy B.M., Nayak N.R, (2012), “ANN Approach for Weather Prediction using Back Propagation”, International Journal of Engineering Trends and Technology- Volume3Issue1- 2012

• Erkaymaz H., Yaşar Ö., (2011), “Yapay Sinir Ağı İle Hava Sıcaklığı Tahmini”,5th International Computer & Instructional Technologies Symposium, 22-24 September 2011 Fırat University, Elazığ- Turkey

• Kaur A., Sharma J.K, Agrawal S., (2011), “Artificial neural networks in forecasting maximum and minimum relative humidity”, International Journal of Computer Science and Network Security, 11(5):197-199

• Maqsood I, Khan MR, Abraham A., (2004) , “Weather Forecasting Models Using Ensembles of Neural Networks”, Neural Comput & App lic 13: 112–122

• Zurada JM, Tan Y, Wang J, (2001),”Nonlinear blind source separation using a radial basis function network”,IEEE Transactions on Neural Networks 12:124–134



yapay sİnİr aĞi İle hava sicaklik tahmİnİ · 21.5.2014 4 •weather temperature was estimated...

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