基于熵权的年降雨量预报优化组合模型研究  

Optimized Combined Model Prediction of Annual Precipitation Based on Entropy Weight

作　　者：王宝红[1] 康永辉 黄伟军[1] 孙凯[1] 解建仓[3] 

机构地区：[1]广西水利电力职业技术学院,广西南宁530023 [2]广西壮族自治区水利电力勘测设计研究院,广西南宁530023 [3]西安理工大学,陕西西安710048

出　　处：《安徽农业科学》2014年第16期5142-5145,共4页Journal of Anhui Agricultural Sciences

基　　金：广西高校优秀人才资助计划(桂教人[2009]62号);2013广西水利科技基金项目"广西北部湾经济区水资源开发利用控制红线制定与动态管理关键技术研究"

摘　　要：鉴于单一预测模型在建模时预测值比实际值存在较大偏差问题,为了提高预测精度,在此首先采用自回归综合移动平均ARIMA模型（简称A模型）、Elman神经网络模型（简称B模型）、小波网络分析模型（简称C模型）、灰色系统GM（1,1）模型（简称D模型）,利用广西田东县1990～ 2007年的年降雨量分别进行了模拟计算,然后在各单一模型预测（拟合）的年降雨量偏差值基础上,应用熵权法对4种模型的偏差值进行客观赋权后优化组合,并根据最优组合结果,选用A、B、C单一模型和最优选的A-B-C优化组合模型对广西田东县2008～ 2010年的年降雨量进行预测对比.结果表明,A、B、C和A-B-C模型得到的均方根误差RMSE和模型效率EF分别为0.018、0.015、0.017、0.013和0.817、0.877、0.843、0.897,优化组合模型的预测精度和拟合度比单一模型的结果得到了提高和改善,该组合方法提高了年降水量的预测精度,为诸如广西田东县以雨养农业为主的区域农业干旱预报提供了新的方法和依据.In view of the fact that a single prediction model＇ s predictive value exists the greater deviation than the actual value,in order to improve the accuracy of the predicted value,this study first adopt the autoregressive integrated moving average ARIMA model （referred to as the A model）,Elman neural network model （referred to as the B model）,the wavelet network analysis model （referred to as the C model）,gray system GM （1,1） model （referred to as the D model） to analyze and forecast respectively utilize 1990 ～ 2010 annual rainfall in Tiandong County,and then on the basis of the annual rainfall deviation value in every single model forecast （fitting）,adopt entropy theory to four model prediction deviation value carrying on objective weighting,and thus to optimize the combination.And in accordance with the optimal combination results,then adopt A model,B model,C model and the most preferred A-B-C optimization combination model respectively to forecast the 2008 ～2010 annual rainfall of Tiandong County,The results show that the root mean square error（RMSE） and model efficiency （EF） of A,B,C and A-B-C model are 0.018,0.015,0.017,0.013 and 0.817,0.877,0.843,0.897 respectively,it can see that the prediction accuracy and goodness of fit of optimization combination model have been upgraded and improved than the single model results,the combination method improve the accuracy of the annual precipitation forecast,and provide a new method and basis for regional agricultural drought prediction such as Guangxi Tiandong County based on mainly rain-fed agriculture.

关 键 词：ARIMA模型 ELMAN神经网络 小波网络分析 熵权 年降雨量 组合预测 

分 类 号：S161.6[农业科学—农业气象学]