Reservoir Inflow Prediction under GCM Scenario Downscaled by Wavelet Transform and Support Vector Machine Hybrid Models

Gusfan Halik,Edijatno Edijatno,Nadjadji Anwar,Budi Santosa

doi:10.1155/2015/515376

Abstract

Climate change has significant impacts on changing precipitation patterns causing the variation of the reservoir inflow. Nowadays, Indonesian hydrologist performs reservoir inflow prediction according to the technical guideline of Pd-T-25-2004-A. This technical guideline does not consider the climate variables directly, resulting in significant deviation to the observation results. This research intends to predict the reservoir inflow using the statistical downscaling (SD) of General Circulation Model (GCM) outputs. The GCM outputs are obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR Reanalysis). A new proposed hybrid SD model named Wavelet Support Vector Machine (WSVM) was utilized. It is a combination of the Multiscale Principal Components Analysis (MSPCA) and nonlinear Support Vector Machine regression. The model was validated at Sutami Reservoir, Indonesia. Training and testing were carried out using data of 1991–2008 and 2008–2012, respectively. The results showed that MSPCA produced better extracting data than PCA. The WSVM generated better reservoir inflow prediction than the one of technical guideline. Moreover, this research also applied WSVM for future reservoir inflow prediction based on GCM ECHAM5 and scenario SRES A1B.

Highlights

Global warming caused by increased concentrations of greenhouse gases has led to climate change
Based on the fourth report of the Intergovernmental Panel on Climate Change (IPCC) [1], the pattern of rainfall and extreme rainfall events in the Southeast Asian countries will change as the climate changes
This study developed direct statistical downscaling models to predict reservoir inflow using a novel hybrid model, namely, Wavelet Support Vector Machine (WSVM)

Summary

Introduction

Global warming caused by increased concentrations of greenhouse gases has led to climate change. It has an impact on changes in rainfall patterns in spatial-temporal perspective. Based on the fourth report of the Intergovernmental Panel on Climate Change (IPCC) [1], the pattern of rainfall and extreme rainfall events in the Southeast Asian countries will change as the climate changes. In Indonesia, rainfall pattern is significantly changed into a spatial-temporal one due to the climate change. Some regions have experienced a timing shift between wet months and dry months [2]. Climate change affects the changes in the trends or patterns of rainfall at the Brantas Watershed in East Java, Indonesia [3]

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Results

Conclusion