Runoff forecasting by artificial neural network and conventional model

被引：64

作者：

Ghumman, A. R. ^{[1
]}

Ghazaw, Yousry M. ^{[1
]}

Sohail, A. R. ^{[2
]}

Watanabe, K. ^{[3
]}

机构：

[1] Al Qassim Univ, Dept Civil Engn, Buraydah, Saudi Arabia

[2] Water Resources Murray Darling Basin Author, Hydrol Modeler, Canberra, ACT, Australia

[3] Saitama Univ, Saitama Package D, Tech Dev Ctr, Sakura Ku, Saitama, Saitama, Japan

来源：

ALEXANDRIA ENGINEERING JOURNAL | 2011年 / 50卷 / 04期

关键词：

Hub River; ANN models; Mathematical models; Low quality data; Runoff analysis;

D O I：

10.1016/j.aej.2012.01.005

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Rainfall runoff models are highly useful for water resources planning and development. In the present study rainfall-runoff model based on Artificial Neural Networks (ANNs) was developed and applied on a watershed in Pakistan. The model was developed to suite the conditions in which the collected dataset is short and the quality of dataset is questionable. The results of ANN models were compared with a mathematical conceptual model. The cross validation approach was adopted for the generalization of ANN models. The precipitation used data was collected from Meteorological Department Karachi Pakistan. The results confirmed that ANN model is an important alternative to conceptual models and it can be used when the range of collected dataset is short and data is of low standard. (C) 2012 Faculty of Engineering, Alexandria University. Production and hosting by Elsevier B.V. All rights reserved.

引用

页码：345 / 350

页数：6

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