多元线性回归与BP神经网络模型在次洪量预测中的对比研究

针对半干旱地区次洪量预测问题,选取岔巴沟流域曹坪水文站1980-2010年中15场洪水资料,根据实测次暴雨、洪量资料,考虑淤地坝控制面积、次暴雨量、暴雨中心位置、前期影响雨量等因子,利用SPSS及MATLAB软件,建立用以预测次洪量的多元线性回归模型和BP神经网络模型。模型预测结果比较表明:多元线性回归模型和BP神经网络模型都能较好地应用于次洪量的预测,进一步得出BP神经网络模型的预测效果优于多元线性回归模型。研究结果可为淤地坝的安全度汛提供决策依据。

Comparative study on multivariate linear regression and BP neural network model in the prediction of flood volume

Aiming at the problem of flood volume forecast in semi-arid area, we selected 15 flood data of the Caoping hydrological station in Chabagou watershed from 1980 to 2010. According to the measured single storm, flood control data in Chabagou watershed, considering the warp land dam area, storm rainfall, rainfall center, antecedent rainfall and others factors, combined with SPSS and MATLAB software, multivariate linear regression model and BP neural network model for the prediction of flood volume were built. The prediction results of the two models show that multivariate linear regression model and BP neural network model can be better applied to flood volume prediction, and the BP neural network model is superior to multivariate linear regression model. Research result can provide decision-making basis for dam safety in the flood season.