- Provide Rainfall prediction, Regression model, Meteorological data, Hyperparameter tuning, Regression analysis
Abstract
This study aims to develop a comprehensive rainfall forecasting system by employing advanced regression models such as CatBoost, XGBoost, Random Forest, SVM, Decision Tree, among others. The primary objectives include identifying, gathering, and preprocessing meteorological and environmental data that influence rainfall patterns. Integration of state-of-the-art regression models is intended to enhance the accuracy of rainfall predictions. The evaluation of these models involves rigorous assessment using performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared. Additionally, a user-friendly interface is designed to facilitate the input of meteorological data, benefiting a wide range of users, including meteorological experts and the general public. The applicability of the system spans across various domains, including agriculture, water resource management, and disaster preparedness.