%0 Journal Article
%A ZHOU Yu-lin
%A MU Zhen-xia
%A PENG Liang
%A GAO Rui
%A YIN Zi-yuan
%A TANG Rui
%T Mid-term and Long-term Hydrological Forecasting of Snowmelt Runoff in Western Tianshan Mountains Based on Mutual Information and Neural Network
%D 2018
%R 10.11988/ckyyb.20170046
%J Journal of Yangtze River Scientific Research Institute
%P 17-21
%V 35
%N 8
%X The aim of this research is to improve the   accuracy of forecasting snowmelt runoff in the mountainous areas of western Tianshan Mountains, and to better support the development of industrial and agricultural production in the study area. The predictor, which is a key issue affecting forecast accuracy, are optimized and selected by using mutual information, correlation coefficient method, and principal component analysis method. The selected predictors are taken as input factors in RBF neural network model and combinatorial wavelet BP neural network model for comparison. Results suggest that: 1) optimized predictors selected by the mutual information method could improve forecast accuracy; 2) according to forecast results under different scenarios, the results of RBF neural network model is superior to those of combinatorial wavelet BP neural network model; 3) with relative error as the standard of accuracy evaluation, RBF neural network model with input factors selected by mutual information method could produce the optimum forecast result.
%U http://ckyyb.crsri.cn/EN/10.11988/ckyyb.20170046