Modeling Rainfall Prediction Framework Using Machine Learning Approach: A Comparative Study

Forecasting rainfall is a critical issue for meteorological departments since it has a big impact on both the economy and human lives. Different measures to deal with natural disasters like floods and drought can be planned depending on the accuracy of rainfall forecasting. It is even more important for a country like India where economic status is immensely agriculture dependent. Because the atmosphere is essentially dynamic, most statistical approaches are unable to predict rainfall with any degree of reasonable precision. Therefore, we rely upon machine learning-based prediction techniques of forecasting using historical data and past patterns. In this work, a variety of regression models, such as Support Vector Regression(SVR), decision trees, linear regression and Random Forests have been studied to find the most efficient model for both temporal and spatial forecasting. Evaluation criteria focus on the R-squared method, for assessing the goodness of fit. The paper underscores the practical implications, emphasizing key areas where accurate weather prediction is vital. It suggests applications in disaster preparedness, agriculture planning, and resource management, thereby emphasizing the potential societal impact of employing linear regression for enhanced weather forecasting.