Using Time-Delay Neural Network Combined with Genetic Algorithms to Predict Runoff Level of Linshan Watershed, Sichuan, China
Publication: Journal of Hydrologic Engineering
Volume 12, Issue 2
Abstract
Runoff simulation and prediction in watersheds is an important and essential step in water management systems, safety yield computations, environmental disposal, design of flood control structures, and so on. In this study, the runoff records of Linshan Watershed, Sichuan Province, PRC, during 1984–1993 are presented and used as samples for predictions. The time-delay neural network (TDNN) model combined with a genetic algorithm is proposed and used to predict the nonlinear relationship and to analyze the characteristics of runoff time series in the Linshan Watershed area. Based on analyzing the whole runoff process—for example, the average, maximum, and standard deviation—during said period, the equal length for training and testing is defined. The optimum TDNN structure of August 20, 2001 has been obtained by gradually increasing the time delay to avoid the limitations of the TDNN model. Comparisons between training and testing show that the forecasting model of the runoff level using TDNN combined with genetic algorithms is generally satisfactory and effective, with slight underpredictions at some points.
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Acknowledgments
The work described in this paper was partially supported by research grants from National Natural Science Foundation of China (Grant No. 50409012), the Research Grants Council of the Hong Kong Special Administrative Region (HKSAR), China (Project No. RGC-CERG/CityU 1253/04E), and Strategic Research Grant No. 7001953(BC) from City University of Hong Kong, HKSAR.
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Information
Published In
Journal of Hydrologic Engineering
Volume 12 • Issue 2 • March 2007
Pages: 231 - 236
Copyright
© 2007 ASCE.
History
Received: Jul 10, 2003
Accepted: Aug 1, 2005
Published online: Mar 1, 2007
Published in print: Mar 2007
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