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Enhancing drought resilience: machine learning–based vulnerability assessment in Uttar Pradesh, India

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Abstract

Drought is a natural and complex climatic hazard. It has both natural and social connotations. The purpose of this study is to use machine learning methods (MLAs) for drought vulnerability (DVM) in Uttar Pradesh, India. There were 18 factors used to determine drought vulnerability, separated into two groups: physical drought and meteorological drought. The study found that the eastern part of Uttar Pradesh is high to very highly prone to drought, which is approximately 31.38% of the area of Uttar Pradesh. The receiver operating characteristic curve (ROC) was then used to evaluate the machine learning models (artificial neural networks). According to the findings, the ANN functioned with AUC values of 0.843. For policy actions to lessen drought sensitivity, DVMs may be valuable. Future exploration may involve refining machine learning algorithms, integrating real-time data sources, and assessing the socio-economic impacts to continually enhance the efficacy of drought resilience strategies in Uttar Pradesh.

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Data availability

All the data and materials related to the manuscript are published with the paper, and available from the first author upon request (barnalimalda29@gmail.com).

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Acknowledgements

The authors sincerely thank the Institute of Eminence, Banaras Hindu University, for their invaluable support through the “Trans Disciplinary Research” Grant (No. R/Dev/IoE/TDR-Projects/2023-24/ 61658) that made this research possible by providing us the structure and laboratory.

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Authors and Affiliations

  1. Department of Geography, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India, 221005

    Barnali Kundu, Narendra Kumar Rana & Sonali Kundu

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  1. Barnali Kundu
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  2. Narendra Kumar Rana
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  3. Sonali Kundu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Barnali Kundu, Sonali Kundu, and Narendra Kumar Rana. The first draft of the manuscript was written by Barnali Kundu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sonali Kundu.

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Kundu, B., Rana, N.K. & Kundu, S. Enhancing drought resilience: machine learning–based vulnerability assessment in Uttar Pradesh, India. Environ Sci Pollut Res 31, 43005–43022 (2024). https://doi.org/10.1007/s11356-024-33776-y

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