Accurate rainfall prediction is crucial for effective water resource management, agricultural planning, and disaster preparedness. Traditional forecasting methods often struggle to capture the complex, non-linear relationships inherent in meteorological data. This research paper explores the application of machine learning regression models to enhance rainfall prediction accuracy. Various models, including Linear Regression, Support Vector Regression (SVR), Random Forest, and Gradient Boosting, were developed and evaluated using historical meteorological data. The performance of these models was assessed using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination. Results indicate that the Gradient Boosting model outperformed traditional statistical methods, demonstrating its effectiveness in capturing the intricate dynamics of rainfall patterns. This study highlights the potential of machine learning techniques to improve rainfall forecasting, providing valuable insights for stakeholders in agriculture, water management, and disaster response. Future work will focus on expanding the dataset to include diverse geographical regions and exploring hybrid models to further enhance predictive accuracy.
Volume 17 | Issue 4
Pages: 19-25