Abstract: Accurate rainfall prediction is critical for water resource management, agriculture, and disaster mitigation. Traditional meteorological models often struggle to account for complex patterns in rainfall data. This paper presents a machine learning-based rainfall prediction system using meteorological data features such as temperature, humidity, pressure, and wind speed. Three models—Linear Regression, Random Forest, and XGBoost—are implemented and compared in terms of accuracy and predictive performance. The study finds that ensemble models such as Random Forest and XGBoost significantly outperform traditional linear models, reducing prediction errors and improving forecast accuracy.
Keywords: Rainfall Prediction, Machine Learning, Weather Forecasting, Meteorological Data, Random Forest, XGBoost, Linear Regression, Time Series Analysis, Feature Importance, Data Preprocessing, Hydrological Forecasting, Climate Modeling, Artificial Intelligence, Ensemble Learning, Prediction Models.
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