GA-Based Support Vector Machine Model for the Prediction of Monthly Reservoir Storage

被引：45

作者：

Su, Jieqiong ^{[1
,2
]}

Wang, Xuan ^{[3
]}

Liang, Yong ^{[4
]}

Chen, Bin ^{[5
]}

机构：

[1] Beijing Normal Univ, Key Lab Water & Sediment Sci, Minist Educ, Sch Environm, Beijing 100875, Peoples R China

[2] Chinese Acad Environm Planning, Minist Environm Protect, Beijing 100012, Peoples R China

[3] Beijing Normal Univ, Key Lab Water & Sediment Sci, Minist Educ, State Key Lab Water Environm Simulat,Sch Environm, Beijing 100875, Peoples R China

[4] Management Off Miyun Reservoir, Beijing 101512, Peoples R China

[5] Beijing Normal Univ, Sch Environm, State Key Lab Water Environm Simulat, Beijing 100875, Peoples R China

来源：

JOURNAL OF HYDROLOGIC ENGINEERING | 2014年 / 19卷 / 07期

基金：

美国国家科学基金会;

关键词：

Prediction model; Reservoir storage; Support vector machine; Genetic algorithm; Miyun Reservoir; ARTIFICIAL NEURAL-NETWORK; PARTICLE SWARM OPTIMIZATION; GENETIC ALGORITHM; FORECASTING SYSTEM; SEDIMENTATION;

D O I：

10.1061/(ASCE)HE.1943-5584.0000915

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Reservoir storage prediction is essential to the operation and management of reservoirs. In this paper, a genetic algorithm (GA)-based support vector machine (SVM) model was developed for the prediction of monthly reservoir storage of Miyun Reservoir (the only surface drinking water source for Beijing city) over the period of 1995 to 2011. At the same time, two other SVM-based models that combine grid search and particle swarm optimization methods respectively for the parameter optimization, were used for comparison. The results showed that the developed GA-SVM model had the best performance in calibration and prediction. Owing to its high accuracy, the GA-SVM model would be popularized to the prediction of reservoir storage in other regions. (C) 2014 American Society of Civil Engineers.

引用

页码：1430 / 1437

页数：8

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