a deep learning approach to downscaling precipitation for
TRANSCRIPT
RESEARCH POSTER PRESENTATION DESIGN © 2015
www.PosterPresentations.com
The objective of this research is to develop a new method for generating high-resolutionprecipitation for hydrological and inundation model using deep learning techniques in data-coarse regions. Therefore, there are three main goals to achieve in the thesis:1. To validate the performance of Convolutional Neural Networks (CNN) for downscaling.2. To compare the CNN results with dynamical downscaling model: Weather Research Forecast(WRF) outputs.
3. To compare the hydrological model results from in situ data and CNN.
OBJECTIVES
INTRODUCTION
METHODOLOGY RESULTS
FUTUREWORK
Yi-ChiaCHANG,Dr.RalphACIERTO,Tomoaki ITAYA,Dr. Akiyuki KAWASAKI,Dr. Ching-PinTUNGDepartment of Civil Engineering, TheUniversityofTokyo, Japan;Department of Bioenvironmental Systems Engineering, NationalTaiwanUniversity, Taiwan
ADeepLearningApproachtoDownscalingPrecipitationforFloodRiskAssessmentoverData-scarceRegion:CaseStudyofBagoRiverBasin,Myanmar
CorrespondingEmail:[email protected]
Future climate data are essential for conducting the hydrological assessment. Due to the scarcescale of climate data generated from General Circulation Models (GCMs), the native-scaleoutputs of climate models could not be directly utilized for basin-scale hydrologic models.Regional Climate Model (RCM) is a dominant technique to dynamical downscaling from theglobal scale to regional scale with consideration of atmospheric physical processes. However,the computational expense of RCM is costly for developing countries. Therefore, the metamodelof RCM for the specific area is eager to be designed to support decision making.
This research introduces novel development and application of Convolutional Neural Network(CNN) to precipitation downscaling over Bago River Basin in Myanmar using in situ data, ERA-Interim (reanalysis data) and Coupled Model Intercomparison Project 5 (CMIP5) as input data toperform Pseudo Global Warming Method (PGWM). The regional climate model WeatherForecasting Research (WRF) is utilized in the research to conduct dynamical downscaling.
Data source:• In situ data• ERA-Interim (1995-2015)• Weather Forecasting Research (WRF)
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JAN FEB MAR APL MAY JUN JUL AUG SEP OCT NOV DEC
AverageMonthlyPrecipitation
1995-2005
BagoRiverBasin,Myanmar
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Figure 1 – Bago River Basin and its climatology
Figure 2 – Research Flowchart
Figure 3 – Convolutional Neural Network Structure
Figure 4 – Comparison of Ground Truth and Simulation Results
Figure 5 – Rainfall-Runoff Inundation (RRI) Flood Simulation Results
• Combine land cover classification from results of satellite images segmentation with flood
hazard map.
• Build deeper neural network architecture to enhance downscaling accuracy.
• Analyze CNN outputs to improve training results and prevent model from overfitting.