Application of ANN and ANFIS models on dryland precipitation prediction (case study: Yazd in central Iran)

被引：30
作者：
Dastorani M.T. [1 ]
Afkhami H. [1 ]
Sharifidarani H. [1 ]
Dastorani M. [1 ]
机构：
[1] Faculty of Natural Resources, Yazd University
[2] Department of Desert Land Management, Faculty of Natural Resources, Tehran University
来源：
Journal of Applied Sciences | 2010年 / 10卷 / 20期
关键词：
Artificial intelligence; Artificial neural networks; Forecasting; Fuzzy logic; Rainfall;
D O I：
10.3923/jas.2010.2387.2394
中图分类号：
学科分类号：
摘要：
The purpose of this research is to evaluate the applicability of two artificial intelligence techniques including Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) in prediction of precipitation amount before its occurrence. In fact, this paper presents the application of these models to predict precipitation in Yazd meteorological station in central Iran with a hyper arid climate condition and veiy low and highly variable annual rainfall. In this study, different architectures of ANN and ANFIS models as well as various combinations of meteorological parameters including 3-year precipitation moving average, maximum temperatures, mean temperatures, relative humidity, mean wind speed, maximum wind direction and evaporation have been used as inputs of the models. According to the results, among different architectures of ANN, dynamic structures including Recurrent Network (RN) and Time Lagged Recurrent Network (TLRN) showed better performance for this application. Final results show that the efficiency of TLRN and ANFIS for this application are almost the same, although in different tests with different input patterns the results produced by these two methods are slightly different. In general, it was found that both ANN and ANFIS models are efficient tool to model and predict precipitation amounts 12 months in advance. © 2010 Asian Network for Scientific Information.
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页码：2387 / 2394
页数：7
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