Abstract
Several industries, including agriculture, water resource management, disaster preparedness, and urban planning, depend heavily on rainfall forecasting. Traditional approaches for predicting rainfall rely on numerical simulations and statistical models, which frequently have accuracy and computing efficiency issues. Growing interest has been shown in using machine learning (ML) algorithms to enhance rainfall prediction as a result of recent developments in ML approaches. In-depth analysis of the methodology, difficulties, and potential applications of machine learning in rainfall prediction is provided in this work.
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