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• W2755188415 abstract "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." @default.
• W2755188415 created "2017-09-25" @default.
• W2755188415 creator A5070176191 @default.
• W2755188415 date "2018-07-01" @default.
• W2755188415 modified "2023-10-18" @default.
• W2755188415 title "Indian summer monsoon rainfall (ISMR) forecasting using time series data: A fuzzy-entropy-neuro based expert system" @default.
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• W2755188415 doi "https://doi.org/10.1016/j.gsf.2017.07.011" @default.
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