改进BP神经网络算法在中小流域洪水预报中的应用研究

Application of improved BP neural network algorithm in flood forecasting in the middle and small watershed

定安河流域位于海南省的中部,是万泉河的一级支流,属于典型的中小流域。针对利用BP神经网络进行洪水预报时预报结果不平滑、冒异常值等问题,在考虑水文过程性质的基础上,提出了多时段综合算法和修匀算法。选取海南省定安河流域作为研究区域,采用深层前向BP神经网络,构建多组预报方案进行对比分析。结果表明,本文所提方法可以弥补原有算法的不足,提高洪水预报精度,作为传统预报方式的有益参照,具有较好的实用价值。

Dingan river watershed,one of the primary tributary of Wanquan River and located at the central part of Hainan Province,is a typical middle and small watershed.Aiming at solving the problems of prediction results unsmooth and in risk presenting outliers when using BP neural network flood forecasting,the multi-time synthesis algorithm and smoothing algorithm are proposed considering the characteristics of hydrological processes.Selecting the Dingan River watershed of Hainan province as the study area and adopting multiplayer feed-forward BP neural network are to build several different plans for comparison and analysis.The results show that the proposed method can be used to compensate for the deficiency of the original algorithm in improving the accuracy of flood forecasting,with a good practical value as a useful reference for the traditional forecasting methods.