Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system

作     者：Pritpal Singh 

作者机构：Smt. Chandaben Mohanbhai Patel Institute of Computer ApplicationsCHARUSAT Campus 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2018年第9卷第4期

页      面：1243-1257页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0708[理学-地球物理学] 0706[理学-大气科学] 0704[理学-天文学] 

基　　金：supported by the Department of Science and Technology (DST)-SERB  Government of India  under Grant EEQ/ 2016/000021 

主　　题：Indian summer monsoon rainfall(ISMR) Fuzzy set Entropy Artificial neural network(ANN) Forecasting 

摘      要：This study presents a model to forecast the Indian summer monsoon rainfall（ISMR）（June-September）based on monthly and seasonal time scales. The ISMR time series data sets are classified into two parts for modeling purposes, viz.,（1） training data set（1871-1960）, and（2） testing data set（1961-2014）.Statistical analyzes reflect the dynamic nature of the ISMR, which couldn＇t be predicted efficiently by statistical and mathematical based models. Therefore, this study suggests the usage of three techniques,viz., fuzzy set, entropy and artificial neural network（ANN）. Based on these techniques, a novel ISMR time series forecasting model is designed to deal with the dynamic nature of the ISMR. This model is verified and validated with training and testing data sets. Various statistical analyzes and comparison studies demonstrate the effectiveness of the proposed model.