A data-driven algorithm for constructing artificial neural network rainfall-runoff models

被引:322
作者
Sudheer, KP [1 ]
Gosain, AK
Ramasastri, KS
机构
[1] Natl Inst Hydrol, DRC, Kakinada 533003, India
[2] Indian Inst Technol, New Delhi 110016, India
[3] Natl Inst Hydrol, Roorkee 247667, Uttar Pradesh, India
关键词
neural network model; input vector; rainfall-runoff modelling;
D O I
10.1002/hyp.554
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
A new approach for designing the network structure in an artificial neural network (ANN)-based rainfall-runoff model is presented. The method utilizes the statistical properties such as cross-, auto- and partial-auto-correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright (C) 2002 John Wiley Sons, Ltd.
引用
收藏
页码:1325 / 1330
页数:6
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