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http://dx.doi.org/10.3741/JKWRA.2012.45.11.1187

A Development of Water Demand Forecasting Model Based on Wavelet Transform and Support Vector Machine  

Kwon, Hyun-Han (Dept. of Civil Engrg., Chonbuk Univ., Disaster Prevention Center)
Kim, Min-Ji (Dept. of Civil Engrg., Chonbuk Univ., Disaster Prevention Center)
Kim, Oon Gi (Disaster & Safety Administration, Jeongeup City Hall)
Publication Information
Journal of Korea Water Resources Association / v.45, no.11, 2012 , pp. 1187-1199 More about this Journal
Abstract
A hybrid forecasting scheme based on wavelet decomposition coupled to a support vector machine model is presented for water demand series that exhibit nonlinear behavior. The use of wavelet transform followed by the SVM model of each leading component is explored as a model for water demand data. The proposed forecasting model yields better results than a traditional ARIMA time series forecasting model in terms of self-prediction problem as well as reproducing the properties of the observed water demand data by making use of the advantages of wavelet transform and SVM model. The proposed model can be used to substantially and significantly improve the water demand forecasting and utilized in a real operation.
Keywords
water demand; wavelet transform; SVM; nonlinear forecasting model;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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