{
  "abstract": "Drought affects more than 60% of India's population, reducing agricultural productivity, straining water resources, and threatening socioeconomic stability. This study proposes an interpretable one-month-ahead meteorological drought prediction framework by integrating class-weighted ensemble machine learning with SHAP and LIME explainable artificial intelligence (XAI). Historical meteorological observations (1972–2022) obtained from the Zenodo climate database were analyzed for 49 districts across five drought-prone Indian states (Bihar, Maharashtra, Gujarat, Andhra Pradesh, and Karnataka). After generating lagged meteorological features (t − 1 to t − 6) and addressing severe class imbalance (7.07% drought events), multiple machine learning classifiers were evaluated. The class-weighted XGBoost model (scale_pos_weight ≈ 11.90, without SMOTE) achieved the best overall performance on the independent 2016–2022 test dataset, with an accuracy of 95.7%, macro precision of 84.7%, macro recall of 81.0%, macro F1-score of 82.7%, and a PR-AUC of 0.647 on the highly imbalanced test set (outperforming both unweighted training and SMOTE-based approaches). SHAP and LIME consistently identified lagged Standardized Precipitation Index (SPI), lagged precipitation, vapour pressure, and wet-day frequency as the most influential drivers of drought occurrence. Exploratory temporal analysis further revealed declining wet-day frequency in Karnataka, increasing vapour pressure in Andhra Pradesh, and substantial climatic variability across the study regions. The proposed framework combines accurate drought prediction with transparent model interpretation, providing a practical decision-support tool for district-level drought early warning, water resource management, and climate-resilient agricultural planning in India.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Megha Rathi"
    },
    {
      "name": "Shailendra Mishra"
    },
    {
      "name": "Naif S. Alshammari"
    },
    {
      "name": "Himani Agrawal"
    },
    {
      "name": "Ehtesham Ashraf"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3gqg7hv0sg25c.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-69632-x",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-69632-x",
  "id": "3gqg7hv0sg25c",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-69632-x",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0",
  "metadata_licence": null,
  "native_subjects": [
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      "lcc",
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      "lcc",
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      "openalex",
      "2306|Hydrology and Drought Analysis"
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  "pdf_url": "https://www.nature.com/articles/s41598-026-69632-x.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/3gqg7hv0sg25c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-69632-x",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-69632-x"
    },
    {
      "first_seen": "2026-10-30T13:45:57Z",
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      "url": "https://doi.org/10.1038/s41598-026-69632-x"
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  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2306|Hydrology and Drought Analysis",
      "subfield": "climate"
    }
  ],
  "title": "Cutting-edge trends in drought prediction and mitigation using ensemble learning and augmented XAI: a meteorological study",
  "title_text": "Cutting-edge trends in drought prediction and mitigation using ensemble learning and augmented XAI: a meteorological study",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:57Z",
  "url": "https://research.draconitic.com/paper/3gqg7hv0sg25c",
  "venue": "Scientific Reports",
  "versions": []
}