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- W4226145550 abstract "Rainfall being one of the key components of the hydrological cycle contributes significantly to assessing flood and drought events. Forecasting rainfall events is vital in field of hydrology and meteorology. A wide range of practical problems has been resolved utilizing multilayer perceptron (MLP). Optimization algorithms assist neural networks in selecting appropriate weights and obtains accurate results. In this study, grey wolves optimization (GWO) meta-heuristic algorithm is used for training MLP for improving accurateness of rainfall forecasting in one rain-gauge station (Silchar) of Cachar district, Assam, India. Performance of hybrid MLP-GWO algorithm is assessed against conventional MLP model using root mean square error (RMSE), coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE). Input parameters such as monthly average temperature, relative humidity, and rainfall data are considered for a time period of 1980–2019 for rainfall forecasting. Results showed that MLP-GWO3 with R2—0.9816, RMSE—38.54, and NSE—0.985 presented the most accurate forecasting in Silchar station. Final results specified that GWO algorithm improved the accurateness of standalone MLP model and can be suggested for forecasting monthly rainfall." @default.
- W4226145550 created "2022-05-05" @default.
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- W4226145550 date "2022-01-01" @default.
- W4226145550 modified "2023-09-28" @default.
- W4226145550 title "Application of Hybrid MLP-GWO for Monthly Rainfall Forecasting in Cachar, Assam: A Case Study" @default.
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- W4226145550 doi "https://doi.org/10.1007/978-981-16-9669-5_28" @default.
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