文章基本信息

标题：Optimum ARX Model Prediction for Monthly Air Temperature Changes in Delta, Egypt
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作者：Mosbeh R. Kaloop ; Mohamed M. Abdelaal ; H.T. El Shambaky 等
期刊名称：Research Journal of Environmental Sciences
印刷版ISSN：1819-3412
电子版ISSN：2152-8238
出版年度：2015
卷号：9
期号：3
页码：137-151
DOI：10.3923/rjes.2015.137.151
出版社：Academic Journals Inc., USA
摘要：This study aims to study the ability application of nonlinear Auto-Regression model with exogeneous inputs (ARX) in forecasting time series monthly temperatures changes in Delta, Egypt for 49 years (1960 to 2009) monitoring data. Three methods are used to estimate the optimal parameters of ARX model identification which are the normalized Least Mean Square (LMS), artificial Neural Network (NN) and Wavenet Neural network (WN). The time series temperature changes from 18 weather stations in Delta are used to compare and estimate the best method for the temperature change models. The models results indicate that the worst case solution for ARX model is LMS while the WN is found to be better than NN in the training period. The NN is found an acceptable performance for training and testing periods. The 95% auto-correlation function for the residuals models shows that there is no loss of information is observed for the applied ARXNN model; however, the ARXNN technique can be successfully used to predict the monthly temperatures of any site at the Delta area in Egypt.