快速BP算法在年径流预测研究中的应用  

Application of fast-speed back propagation neural network to annual runoff forecast

作　　者：陆玉娇[1] 赵雪花[1] 安莉莉[1] 

机构地区：[1]太原理工大学水利科学与工程学院,山西太原030024

出　　处：《水资源与水工程学报》2012年第4期95-97,共3页Journal of Water Resources and Water Engineering

基　　金：国家自然科学基金项目(40901018);山西省高等学校优秀青年学术带头人支持计划资助

摘　　要：中长期径流预测是水资源研究领域的一项重要内容,本文针对汾河上游兰村站的径流量进行预测。建立三层BP神经网络模型,采用Levenberg-Marquardt(LM)法对模型进行训练。结果表明:模拟和预测的结果精度较高,满足精度要求。LM-BP神经网络模型在汾河上游兰村站的径流预测中是可行的,研究结果可为区域水资源规划管理提供科学依据。Medium and long term hydrologic prediction of runoff is one of the most important subjects in the field of water research.This article aimed at predicting runoff at Lancun hydrologic station in the upper reaches of the Fenhe River.Artificial neural network is a nonlinear dynamic system composed of a large number of neurons.Runoff material recorded at the Lancun hydrologic station in the upper reaches of the Fenhe River were used to analyze and predict by three layers back propagation(BP) neural network model.Levenberg-Marquardt method was used to train model.Prediction results meet accuracy requirements,indicating that it is feasible to predict runoff using improved BP neural network model at Lancun hydrologic station in the upper reaches of the Fen River.

关 键 词：快速BP算法 年径流预测 汾河上游 

分 类 号：TV1[水利工程]