Vol. 11 No. 1 (2024): Vol 11, Iss 1, Year 2024
Articles

Exploring rainfall prediction through regression models: A systematic literature review

Velmurugan S
Department of Computer Science, Kongunadu Arts and Science College, Coimbatore -641029, Tamil Nadu, India
Saravana Moorthy R
Department of Computer Science, Kongunadu Arts and Science College, Coimbatore -641029, Tamil Nadu, India
Subramanian K
Department of IT and Analytics, Xavier Institute of Management and Entrepreneurship (XIME), Bangalore, India
Angel S
Department of Computer Science (SF), Avanishilingam University, Coimbatore, Tamil Nadu, India
Published June 30, 2024
Keywords
  • Provide Rainfall prediction, Regression model, Meteorological data, Hyperparameter tuning, Regression analysis
How to Cite
Velmurugan S, Saravana Moorthy R, Subramanian K, & Angel S. (2024). Exploring rainfall prediction through regression models: A systematic literature review. Kongunadu Research Journal, 11(1), 9 - 14. https://doi.org/10.26524/krj.2024.2

Abstract

This study aims to develop a comprehensive rainfall forecasting system by employing advanced regression models such as CatBoost, XGBoost, Random Forest, SVM, Decision Tree, among others. The primary objectives include identifying, gathering, and preprocessing meteorological and environmental data that influence rainfall patterns. Integration of state-of-the-art regression models is intended to enhance the accuracy of rainfall predictions. The evaluation of these models involves rigorous assessment using performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared. Additionally, a user-friendly interface is designed to facilitate the input of meteorological data, benefiting a wide range of users, including meteorological experts and the general public. The applicability of the system spans across various domains, including agriculture, water resource management, and disaster preparedness.

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