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

Author

Listed:
  • Vijendra Kumar

    (Dr. Vishwanath Karad MIT World Peace University)

  • Naresh Kedam

    (Samara National Research University)

  • Ozgur Kisi

    (Luebeck University of Applied Sciences
    Ilia State University)

  • Saleh Alsulamy

    (King Khalid University)

  • Khaled Mohamed Khedher

    (King Khalid University)

  • Mohamed Abdelaziz Salem

    (King Khalid University)

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.

Suggested Citation

  • Vijendra Kumar & Naresh Kedam & Ozgur Kisi & Saleh Alsulamy & Khaled Mohamed Khedher & Mohamed Abdelaziz Salem, 2025. "A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(1), pages 271-290, January.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:1:d:10.1007_s11269-024-03969-8
    DOI: 10.1007/s11269-024-03969-8
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    References listed on IDEAS

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    1. Fabio Di Nunno & Francesco Granata & Quoc Bao Pham & Giovanni de Marinis, 2022. "Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    2. Xiaoliang Xie & Bingqi Xie & Jiaqi Cheng & Qi Chu & Thomas Dooling, 2021. "A simple Monte Carlo method for estimating the chance of a cyclone impact," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(3), pages 2573-2582, July.
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