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 ...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.展开更多
基金supported by the Department of Science and Technology (DST)-SERB, Government of India, under Grant EEQ/ 2016/000021
文摘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.
