Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction

查看全文 作　　者：S.Renuga [1]Devi;[1]P.Arulmozhivarman;[2]C.Venkatesh;Pranay [1]Agarwal

机构地区：[1]School of Electronics Engineering,VIT University,Vellore 632 014,Tamil Nadu,India;[2]Faculty of Engineering,EBET Group of Institutions,Kangayam,Tamil Nadu,India

出　　处：《International Journal of Automation and computing》索引2016年第13卷第5期,共11页

摘　　要：With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network(BPN), cascade-forward back propagation neural network(CBPN), distributed time delay neural network(DTDNN) and nonlinear autoregressive exogenous network(NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor(a taluk of Nilgiris). Based on the performance analysis,NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors. 关键词：人工神经网络模型降水量预测性能比较反向传播神经网络 Marquardt 非线性自回归时滞神经网络梯度下降方法

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Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction

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耦合文献(4)

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