Using time-delay neural network combined with genetic algorithms to predict runoff level of Linshan Watershed, Sichuan, China

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. © 2007 ASCE.