为提高天山西部山区融雪径流的预报精度,更好地指导所在区域的工农业生产发展,针对影响预报精度的关键问题(预报因子的选择),基于互信息法、相关系数法、主成分分析法对研究区的预报因子进行优选,采用RBF神经网络以及组合小波BP神经网络模型进行径流预报研究,并进行不同方案的比较。结果表明:①互信息法优选出的预报因子作为模型输入可以提高预报精度;②采用不同优选预报因子作为RBF神经网络以及组合小波BP神经网络模型的输入变量,结果表明RBF神经网络模型的预测精度要好于组合小波BP神经网络模型;③以相对误差作为评价模型精确度的标准,预测效果最好的是基于互信息方法挑选出的预报因子作为RBF神经模型输入数据的模型预测结果。

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.