Discharge rating curve extension - A new approach

被引:81
作者
Sivapragasam, C [1 ]
Muttil, N
机构
[1] Mepco Schlenk Engn Coll, Dept Civil Engn, Tamil Nadu 626005, India
[2] Univ Hong Kong, Dept Civil Engn, Hong Kong, Hong Kong, Peoples R China
关键词
artificial neural network; forecasting; rating curve; regression; support vector machine;
D O I
10.1007/s11269-005-6811-2
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
It is often necessary to have stage discharge curve extended (extrapolated) beyond the highest (and sometimes lowest) measured discharges, for river forecasting, flood control and water supply for agricultural/industrial uses. During the floods or high stages, the river may become inaccessible for discharge measurement. Rating curves are usually extended using log-log axes, which are reported to have a number of problems. This paper suggests the use of Support Vector Machine (SVM) in the extrapolation of rating curves, which works on the principle of linear regression on a higher dimensional feature space. SVM is applied to extend the rating curves developed at three gauging stations in Washington, namely Chehalis River at Dryad and Morse Creek at Four Seasons Ranch (for extension of high stages) and Bear Branch near Naselle (for extension of low stages). The results obtained are significantly better as compared with widely used logarithmic method and higher order polynomial fitting method. A comparison of SVM results with ANN (Artificial Neural Network) indicates that SVM is better suited for extrapolation.
引用
收藏
页码:505 / 520
页数:16
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