南水北调与水利科技2016，Vol.14Issue(6)：12-17,26,7.DOI:10.13476/j.cnki.nsbdqk.2016.06.003
ANN 、ANFIS 和 AR 模型在日径流时间序列预测中的应用比较
Comparative study of ANN,ANFIS and AR model for daily runoff time series prediction
摘要
Abstract
Hydrological prediction is an important aspect of hydrology′s service for economic and society .The prediction result not only provides decision support for reservoir generation operation ,but also is of great significance to the economical operation of hydropower systems ,navigation ,flood control and so on .The autoregressive model (AR model) ,artificial neural network (ANN) and adaptive neural fuzzy inference system (ANFIS) have been widely applied in the daily runoff time series prediction . In this paper ,these three models were applied in daily runoff prediction at Tongzilin station .Nash‐Sutcliffe efficiency coefficient (NS coefficient) ,root mean square error (RMSE) and mean absolute relative error (MARE) were used to evaluate the perform‐ances of three models .Threshold statistics index was used to analyze prediction error distribution of three models .At the same time ,the prediction ability of three models was studied by gradually increasing the prediction period .The results showed that ANFIS had not only better simulation ability and generalization ability ,but also better model performance in the same prediction period compared to ANN and AR model .As a result ,ANFIS can be a recommended prediction model for daily runoff time se‐ries .关键词
自回归模型/人工神经网络/自适应神经模糊推理系统/日径流时间序列预测Key words
autoregressive model/artificial neural network/adaptive neural fuzzy inference system/daily runoff prediction分类
地球科学引用本文复制引用
谭乔凤,王旭,王浩,雷晓辉..ANN 、ANFIS 和 AR 模型在日径流时间序列预测中的应用比较[J].南水北调与水利科技,2016,14(6):12-17,26,7.基金项目
“十二五”国家科技支撑计划项目（2013BAB05B00） Fund12th Five-Year Science and Technology Support Program （）
