RAINFALL PREDICTION USING MACHINE LEARNING

Abstract

Rainfall prediction plays a crucial role in agriculture, water resource management, disaster prevention, and climate analysis. Accurate forecasting of rainfall helps farmers make informed decisions, supports efficient irrigation planning, and aids governments in managing floods and droughts. Traditional statistical and meteorological methods often struggle to capture complex weather patterns due to the nonlinear and dynamic nature of atmospheric conditions. To address these limitations, this project proposes a machine learning-based approach for rainfall prediction using historical weather data. The proposed system utilizes various meteorological parameters such as temperature, humidity, wind speed, atmospheric pressure, and previous rainfall records as input features. The data is preprocessed through cleaning, normalization, and handling missing values to improve model performance. Multiple machine learning algorithms, including Linear Regression, Decision Trees, Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN), are applied to analyze patterns in the data and predict rainfall occurrence or quantity. Among these models, ensemble methods like Random Forest often provide higher accuracy due to their ability to handle nonlinear relationships and reduce overfitting. Experimental results demonstrate that machine learning models significantly improve prediction accuracy compared to traditional methods. The system is capable of identifying complex relationships between weather parameters and rainfall patterns, providing reliable forecasts. However, challenges such as data quality, seasonal variability, and computational complexity remain. Overall, the proposed approach offers a scalable and efficient solution for rainfall prediction, contributing to better decision-making in agriculture and environmental management.