A comparison of machine learning models for predicting rainfall in urban metropolitan cities
Current research studies offer an investigation of machine learning methods used for
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their
temporal complexities, are exploited using a unique data segmentation approach, providing
discrete training, validation, and testing sets. Two unique models are created: Model-1,
which is based on daily data, and Model-2, which is based on weekly data. A variety of
performance criteria are used to rigorously analyze these models. CatBoost, XGBoost …
forecasting rainfall in urban metropolitan cities. Time series data, distinguished by their
temporal complexities, are exploited using a unique data segmentation approach, providing
discrete training, validation, and testing sets. Two unique models are created: Model-1,
which is based on daily data, and Model-2, which is based on weekly data. A variety of
performance criteria are used to rigorously analyze these models. CatBoost, XGBoost …
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