{
  "abstract": "Climate change has accelerated devastating impacts across the globe, and heatwaves are among the most severe climate extremes threatening human health, agricultural productivity, and ecosystems in northwestern Nigeria. This study aimed to develop a long short-term memory (LSTM) based model to predict heatwave trends in northwestern Nigeria. Historical meteorological data, including daily maximum temperature, minimum temperature, and relative humidity from 1980 to 2024, were sourced from the Nigerian Meteorological Agency (NiMet). Heatwave intensity was measured via heat index (HI) computation, whereas temporal trends were analyzed via the Modified Mann Kendall (MMK) test and Innovative Trend Analysis (ITA). Spatial variation in heatwave intensity was mapped via the Inverse Distance Weighting (IDW) method. The LSTM model demonstrated high predictive accuracy during the testing phase, with correlation coefficients (R) ranging from 0.87 to 0.91 and coefficients of determination (R2) ranging from 0.76 to 0.83. The findings revealed a pronounced increase in the frequency and severity of heatwaves across northwestern Nigeria. These findings demonstrate the ability of LSTM based forecasting to provide reliable heatwave predictions and highlight the potential of LSTM models for supporting regional climate adaptation strategies, heat early warning systems, and evidence-based decision making.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Geography and Sustainability Studies, Kaduna State University, Kaduna, Nigeria",
    "Climate Research Group, Kaduna State University, Kaduna, Nigeria"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Saadatu Umaru Baba"
    },
    {
      "affs": [
        1
      ],
      "name": "Abu-hanifa Babati"
    },
    {
      "name": "Zaharaddeen Isa"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/61z1gxkmp3chk.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pclm.0001005",
  "fulltext_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0001005",
  "id": "61z1gxkmp3chk",
  "issns": [
    "2767-3200"
  ],
  "landing_url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0001005",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2306|Climate variability and models"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Climatology/Climate change"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Climatology/Climate modeling"
    ],
    [
      "plos",
      "/Earth sciences/Atmospheric science/Meteorology/Humidity"
    ],
    [
      "plos",
      "/Earth sciences/Seasons"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Signs and symptoms/Hyperthermia"
    ],
    [
      "plos",
      "/People and places/Geographical locations/Africa/Nigeria"
    ],
    [
      "plos",
      "/Physical sciences/Physics/Classical mechanics/Mechanical stress/Thermal stresses"
    ],
    [
      "plos",
      "/Research and analysis methods/Simulation and modeling/Climate modeling"
    ]
  ],
  "openalex": "W7214213334",
  "pdf_url": "https://journals.plos.org/climate/article/file?id=10.1371/journal.pclm.0001005&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/61z1gxkmp3chk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:43Z",
      "native_id": "10.1371/journal.pclm.0001005",
      "source": "plos",
      "url": "https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0001005"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "plos:meteorology",
      "subfield": "atmospheric"
    },
    {
      "field": "earth",
      "origin": "plos:climate change",
      "subfield": "climate"
    }
  ],
  "title": "Long Short-Term Memory (LSTM)-based prediction of heatwave trends in northwestern Nigeria",
  "title_text": "Long Short-Term Memory (LSTM)-based prediction of heatwave trends in northwestern Nigeria",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:43Z",
  "url": "https://research.draconitic.com/paper/61z1gxkmp3chk",
  "venue": "PLOS Climate",
  "versions": []
}