Machine Learning Approach for Rainfall Prediction Using Multiple Regression Techniques

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.

Introduction

Accurate rainfall prediction is essential for agriculture, water resource management, disaster prevention, and environmental sustainability. Traditional statistical forecasting methods often struggle to capture complex nonlinear relationships among atmospheric variables. Recent advancements in Artificial Intelligence and Machine Learning (ML) have improved rainfall forecasting by analyzing historical weather data and identifying hidden patterns.

This study proposes a machine learning-based rainfall prediction system using Multiple Linear Regression (MLR), Random Forest Regressor, and K-Nearest Neighbor (KNN) Regression algorithms. Historical meteorological data containing parameters such as temperature, humidity, pressure, wind speed, cloud cover, sunshine duration, evaporation, and previous rainfall values are used for training and testing. Data preprocessing techniques, including missing value handling, normalization, duplicate removal, and feature selection, are applied to improve model performance.

The literature review highlights that machine learning approaches, especially ensemble techniques like Random Forest, provide better prediction accuracy compared to traditional statistical models. Existing systems face challenges such as low accuracy under nonlinear weather conditions, sensitivity to noisy data, high computational requirements, and limited scalability. The proposed system addresses these limitations by applying efficient preprocessing and comparing multiple regression models.

The methodology involves dataset collection, preprocessing, feature selection, data splitting, model training, rainfall prediction, and performance evaluation. The dataset is divided into 70% training and 30% testing data, and models are evaluated using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² Score.

Experimental results show that the Random Forest Regressor performs better than Multiple Linear Regression and KNN Regression because it can effectively model nonlinear relationships between weather parameters while reducing overfitting. The proposed rainfall prediction framework provides an intelligent and reliable solution that can support farmers, government organizations, environmental researchers, and disaster management authorities in making better decisions.

Conclusion

Rainfall prediction is an important application of machine learning that supports agriculture, water resource management, disaster preparedness, and environmental planning. In this work, a machine learning-based rainfall prediction system was developed using Multiple Linear Regression (MLR), Random Forest Regressor (RFR), and K-Nearest Neighbor (KNN) Regression algorithms. The proposed framework includes data preprocessing, feature selection, model training, and performance evaluation to improve the accuracy of rainfall prediction. Experimental analysis demonstrated that the Random Forest Regressor produced superior performance compared with Multiple Linear Regression and KNN Regression. The model achieved lower prediction errors and a higher coefficient of determination due to its ensemble learning capability and robustness against overfitting. Proper preprocessing of meteorological data further enhanced prediction accuracy by eliminating inconsistencies and improving data quality. The proposed system provides an efficient, reliable, and scalable rainfall prediction solution that can assist farmers, researchers, meteorological departments, and disaster management authorities in making informed decisions. The integration of machine learning techniques into weather forecasting has significant potential to improve prediction accuracy and support sustainable resource management.

References

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Copyright

Copyright © 2026 Paina Suvarna Latha , Doma Gangadhar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.