Machine Learning Approach for Rainfall Prediction Using Multiple Regression Techniques
TL;DR
Experimental results demonstrate that the Random Forest Regressor achieves superior prediction performance compared with Multiple Linear Regression and KNN Regression by effectively handling nonlinear relationships and reducing prediction errors.
Abstract
Rainfall prediction is one of the most significant research areas in meteorology because of its direct influence on agriculture, water resource management, disaster preparedness, and environmental sustainability. Accurate rainfall forecasting assists governments, farmers, and disaster management agencies in making informed decisions regarding crop planning, irrigation scheduling, flood prevention, and drought management. Conventional statistical forecasting methods often fail to capture the complex nonlinear relationships existing among climatic variables, leading to inaccurate predictions. Machine Learning (ML) techniques have emerged as efficient alternatives by automatically learning hidden patterns from historical weather datasets. This paper presents a machine learning-based rainfall prediction system using Multiple Linear Regression (MLR), Random Forest Regressor, and K-Nearest Neighbor (KNN) Regression algorithms. The proposed model utilizes historical meteorological parameters such as temperature, humidity, atmospheric pressure, wind speed, and previous rainfall records to predict future rainfall with improved accuracy. Initially, the collected dataset undergoes preprocessing techniques including missing value handling, normalization, feature selection, and data transformation. The processed dataset is divided into training and testing subsets for model development and validation. The performance of each regression algorithm is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R² Score, and prediction accuracy. Experimental results demonstrate that the Random Forest Regressor achieves superior prediction performance compared with Multiple Linear Regression and KNN Regression by effectively handling nonlinear relationships and reducing prediction errors. The proposed system provides an efficient and reliable rainfall forecasting solution that can support agricultural planning and environmental monitoring.