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Performance Comparison of Artificial Neural Network Models for Daily Rainfall Prediction 被引量:3
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作者 S.Renuga Devi P.Arulmozhivarman +1 位作者 C.Venkatesh Pranay Agarwal 《International Journal of Automation and computing》 EI CSCD 2016年第5期417-427,共11页
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 (C... 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. 展开更多
关键词 Rainfall prediction artificial neural networks distributed time delay neural network cascade-forward back propagation network nonlinear autoregressive exogenous network.
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