Hybrid Neural Network Models for Hydrologic Time Series Forecasting Based on Genetic Algorithm
Hydrologic time series forecasting is very an important area in water resource. Based on the multitime scale and the nonlinear characteristics of the rainfall-runoff time series, a new hybrid neural network (NN) has been suggested by Genetic Algorithm (GA) selection the lag period of time series for NN input variables, optimization neural network architecture and connection weights. The evolved neural network architecture and connection weights are then input into a new neural network. The new neural network is trained using back-propagation (BP) algorithm for hydrologic time series forecasting. The ensemble strategy is implemented using the quadratic programming. The present model absorbs some merits of GA and artificial neural network. Case studies, the short and long term prediction of hydrological time series, have been researched. The comparison results revealed that the suggested model could increase the forecasted accuracy and prolong the length time of prediction.
time series forecasting feature selection neural network genetic algorithm optimization
Ganji Huang Lingzhi Wang
College of Mathematics and Information Science Guangxi University Nanning, Guangxi, 530044, China Department of Mathematics and Computer Science Liuzhou Teachers College Uuzhou, Guangxi, 545003, Chi
国际会议
昆明、丽江
英文
1347-1350
2011-04-15（万方平台首次上网日期，不代表论文的发表时间）