基于弹性梯度下降算法的BP神经网络降雨径流预报模型

Research on BP neural network rainfall runoff forecasting model based on elastic gradient descent algorithm

运用反向传播(back propagation,BP)的改进算法弹性梯度下降算法,选择崇阳溪上游流域1997—2014年的14场降雨径流过程,以流域内洋庄、吴边、大安、坑口、岭阳、岚谷6个雨量站的实测降雨量和武夷山水文站的前期流量资料为输入,武夷山水文站相应流量为输出,建立弹性梯度下降算法的BP神经网络降雨径流预报模型,采用7场降雨径流过程对模型进行检验。结果表明,与传统的反向传播算法相比,该模型所需的参数较少,运算速度显著提高,模型的预报精度满足要求,可以为防汛部门预测洪水提供依据。

The improved elastic gradient descent algorithm of back propagation was used,and 14 rainfall runoff processes from 1997 to 2014 in the upper reaches of Chongyang River were selected.The back propagation(BP)neural network rainfall-runoff forecasting model of the elastic gradient descent algorithm was established,which took the measured rainfall of six rainfall stations in Yangzhuang,Wubian,Da'an,Kengkou,Lingyang,and Langu in the basin and the preliminary flow data of Wuyishan Hydrological Station as inputs,and selected the corresponding flow of Wuyishan Hydrological Station as output.The 7-rainfall runoff process was used to test the model,the test results showed that the proposed method required fewer parameters and had higher operation speed than the traditional back propagation algorithm.The prediction accuracy of the model could meet the requirements,and provide the basis for flood control and disaster reduction.