A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting

Vijendra Kumar (), Naresh Kedam (), Ozgur Kisi (), Saleh Alsulamy (), Khaled Mohamed Khedher () and Mohamed Abdelaziz Salem ()
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Vijendra Kumar: Dr. Vishwanath Karad MIT World Peace University
Naresh Kedam: Samara National Research University
Ozgur Kisi: Luebeck University of Applied Sciences
Saleh Alsulamy: King Khalid University
Khaled Mohamed Khedher: King Khalid University
Mohamed Abdelaziz Salem: King Khalid University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 1, No 14, 290 pages

Abstract: Abstract Accurate rainfall forecasting is crucial for various sectors across diverse geographical regions, including Uttarakhand, Uttar Pradesh, Haryana, Punjab, Himachal Pradesh, Madhya Pradesh, Rajasthan, and the Union Territory of Delhi. This study addresses the need for precise rainfall predictions by bridging the gap between localized meteorological data and broader regional influences. It explores how rainfall patterns in neighboring states affect Delhi's precipitation, aiming to improve forecasting accuracy. Historical rainfall data from neighboring states over four decades (1980–2021) were collected and analyzed. The study employs a dual-model approach: a daily model for immediate rainfall triggers and a weekly model for longer-term trends. Several machine learning algorithms, including CatBoost, XGBoost, ElasticNet, Lasso, LGBM, Random Forest, Multilayer Perceptron, Ridge, Stochastic Gradient Descent, and Linear Regression, were used in the modeling process. These models were rigorously assessed based on performance metrics from training, validation, and testing datasets. For daily rainfall forecasting, CatBoost, XGBoost, and Random Forest emerged as top performers, showcasing exceptional accuracy and pattern-capturing capabilities. In weekly rainfall forecasting, XGBoost consistently achieved near-perfect accuracy with an R2 value of 0.99, with Random Forest and CatBoost also demonstrating strong performance. The study provides valuable insights into how climate patterns in neighboring states influence Delhi's weather, leading to more reliable and timely rainfall predictions.

Keywords: Rainfall forecasting; Water resource management; Precipitation patterns; Machine Learning; CatBoost; XGBoost (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-024-03969-8

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