RAINFALL PREDICTION USING MACHINE LEARNING

Accurate rainfall prediction is crucial for sectors like agriculture, disaster management, water resource planning, and climate adaptation. However, forecasting rainfall remains a challenge due to the unpredictable nature of atmospheric conditions. In recent years, machine learning (ML) has proven to be a valuable tool in analyzing complex meteorological data, offering an advanced alternative to traditional statistical models. This study explores the use of machine learning techniques for rainfall prediction through MATLAB, a powerful platform for data analysis, algorithm development, and model implementation. Different ML models, such as regression techniques, support vector machines (SVM), decision trees, and neural networks, are applied to analyze historical meteorological data. The models incorporate key features such as temperature, humidity, wind speed, atmospheric pressure, and past rainfall records to enhance predictive accuracy. To optimize results, preprocessing techniques such as normalization, feature selection, and handling missing values are employed. Furthermore, the framework is designed to accommodate large datasets and real-time data, making it scalable and adaptable to various geographical regions and climatic conditions.