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Development of Ensemble Probabilistic Machine Learning Models for Rainfall Predictions

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Advances in Mathematical Modelling, Applied Analysis and Computation (ICMMAAC 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 953))

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Abstract

Rain is of paramount importance for Indian agriculture, as it serves as the primary source of water for crops, sustaining agricultural productivity and ensuring food security for millions of people. In India’s predominantly rain-fed agriculture, timely and adequate rainfall is crucial for successful crop growth, making it a lifeline for farmers and a determining factor in the country’s agricultural output.

Accurate rainfall predictions are essential for various applications, including agriculture, water resource management, and disaster preparedness. Ensemble machine learning models have demonstrated their capability to enhance the accuracy and reliability of rainfall predictions compared to single models. This article presents a comparative analysis of different ensemble techniques applied to rainfall prediction tasks. We explore various ensemble approaches, including Averaging, Max Voting, and Stacking, and evaluate their performance using rainfall day-wise datasets from Pantnagar (29.0222° N, 79.4908° E), Uttarakhand, India, from 2010 to 2022.

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Authors and Affiliations

  1. Department of Mathematics, Statistics and Computer Science, G.B. Pant University of Agriculture and Technology, Pantnagar, India

    Tanuja Mathpal, Ravindra Kumar Singh Rajput, Bharti Kunwar,  Dibyanshu & Sourav Pandey

Authors
  1. Tanuja Mathpal
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  2. Ravindra Kumar Singh Rajput
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  3. Bharti Kunwar
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  4. Dibyanshu
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  5. Sourav Pandey
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Corresponding author

Correspondence to Ravindra Kumar Singh Rajput .

Editor information

Editors and Affiliations

  1. Department of Mathematics, JECRC University, Jaipur, Rajasthan, India

    Jagdev Singh

  2. Department of Mathematics, University of Memphis, Memphis, TN, USA

    George A. Anastassiou

  3. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

    Dumitru Baleanu

  4. Department of Mathematics, University of Rajasthan, Jaipur, Rajasthan, India

    Devendra Kumar

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Mathpal, T., Rajput, R.K.S., Kunwar, B., Dibyanshu, Pandey, S. (2024). Development of Ensemble Probabilistic Machine Learning Models for Rainfall Predictions. In: Singh, J., Anastassiou, G.A., Baleanu, D., Kumar, D. (eds) Advances in Mathematical Modelling, Applied Analysis and Computation . ICMMAAC 2023. Lecture Notes in Networks and Systems, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-031-56304-1_11

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  • DOI: https://doi.org/10.1007/978-3-031-56304-1_11

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  • Print ISBN: 978-3-031-56303-4

  • Online ISBN: 978-3-031-56304-1

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