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Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India

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Abstract

Standardized precipitation index prediction and monitoring are essential to mitigating the effect of drought actions on precision farming, environments, climate-smart agriculture, and the water cycle. In this study, four data-driven models, additive regression, random subspace, M5Pruned (M5P), and bagging tree models, were adopted to predict the standardized precipitation index (SPI) at the Upper Godavari Basin for various periods (3 months, 6 months, and 12 months). The data-driven models’ input data was pre-processed with machine learning models to increase quality and the model’s performance a priori. These four models predicted the SPI-3, SPI-6, and SPI-12 months based on three metrological station data. Based on the statistical performance metrics such as correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE), our findings showed that the bagging was the best model for predicting SPI-3 and SPI-6 while the M5P the best for SPI-12 estimation in station 1, while in stations 2 and 3, M5P was superlative in predicting the SPI-3 and SPI-12 months, and the bagging was best in SPI-6. All the best models had acceptable mid-term drought forecasting based on the SPI-3, SPI-6, and SPI-12 months for three stations in the Upper Godavari Basin in India. The machine learning models created in this study produced satisfactory results in short-term and mid-term drought forecasting, and it will be a new strategy for water developers and planners to use for future management and scheduling.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Thanks to the NASA POWER, Prediction of Worldwide Energy Resources (https://power.larc.nasa.gov/), for providing the data needed in this research

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Authors and Affiliations

  1. Indian Institute of Tropical Meteorology, Pune, India

    Chaitanya B. Pande

  2. Institute of Energy of Infrastructure, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia

    Chaitanya B. Pande

  3. Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania

    Romulus Costache

  4. Danube Delta National Institute for Research and Development, 165 Babadag, 820112, Tulcea, Romania

    Romulus Costache

  5. Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq

    Saad Sh. Sammen

  6. Department of Agricultural Engineering, Bahuddin Zakariya University, Multan, 34200, Pakistan

    Rabeea Noor

  7. Agricultural Engineering Dept, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt

    Ahmed Elbeltagi

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Contributions

Ahmed Elbeltagi and Chaitanya B. Pande had the original idea of the research. Chaitanya B. Pande: Conceptualization, Development of Methodology, Formal analysis, Original writing and drafting, Writing—review and editing. Ahmed Elbeltagi: Conceptualization, Formal analysis, Software, Writing—review and editing. Romulus Costache, Saad Sh. Sammen, Rabeea Noor: Original writing and drafting, Writing—review and editing. All authors approved the final version for submission.

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Correspondence to Ahmed Elbeltagi.

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Pande, C.B., Costache, R., Sammen, S.S. et al. Combination of data-driven models and best subset regression for predicting the standardized precipitation index (SPI) at the Upper Godavari Basin in India. Theor Appl Climatol 152, 535–558 (2023). https://doi.org/10.1007/s00704-023-04426-z

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