塔里木河流域径流量预测分析

因径流量预测中伴随着随机性、灰信息性的特点,传统的数学模型难以解决复杂的非结构化问题。在参与塔河流域多个水文控制站点径流量预测的基础上,探讨了GM(1,1)与BP算法在径流量预测中的有效性及存在的一些问题。首先用灰色关联理论对相关序列的灰色绝对、相对、综合关联度进行了分析;然后建立了GM(1,1)模型,讨论了在原始序列准光滑条件满足、1-AGO准指数规律满足条件下GM(1,1)模型模拟随机振荡序列的局限;最后建立了神经网络模型,应用BP算法训练网络权矩阵和偏置,对径流量的历史演变进行了仿真模拟,对预留年份的径流量作了预测检验。

Analysis on Forecasting Runoff in Tarim River Basin

Due to the characteristics of randomicity and gray information in forecasting runoff,the traditional models were quite difficult to solve those complex and non-structural problems.Based on the runoff forecasting of some gauge stations in Tarim River Basin participated,the validity and some problems of GM(1,1) and BP algorithm existed in runoff forecasting were discussed.In the paper,the gray absolute,relative and integrated correlation degree between characteristic series and factors' series were firstly analyzed using gray correlation theory.And then,a GM(1,1) was established.The limitation of simulating a stochastic and oscillating sequence through GM(1,1) under the condition of the satisfaction of quasi-smoothness of original sequence and the satisfaction of quasi-exponential law of 1-Accumulating Generation Operator was talked over.In the end,an artificial neural network model was set up.The weight matrices and biases of the network were trained using BP algorithm.The historical evolvement of runoff was simulated and the runoff of reserved years were predicted and checked up.