{
  "abstract": "Existing forest fire risk prediction methods often focus on single-element analysis, which makes it difficult to effectively capture the underlying mechanisms of the “fuel-climate” interaction. This paper proposes a physically aligned prediction framework named FWI-MSNet. Using 18 years of synchronized observation data from the Huitong Ecological Station in China, the framework constructs a multi-scale feature system, selecting 21 physically relevant key features, including core indicators of the Forest Fire Weather Index (FWI). A parallel multiscale 1-Dimensional Convolutional Neural Network (1D-CNN) extracts dynamic features across multiple temporal scales (from daily to seasonal). Subsequently, a synergistic mechanism integrating Gated Recurrent Units (GRU) and Transformer achieves deep integration of temporal evolution and global correlation. Experimental results demonstrate that compared to seven baseline models (including XGBoost, LSTM, CNN, and four ablation variants), the mean of RMSE, MAE, and MAPE decreases by 56.1%, and R2 improves from 0.223 (baseline average) to 0.9251. Furthermore, it accurately captures the trajectory of fire risk evolution in the 2020 catastrophic forest fires in Australia. This study provides a valuable reference for forest fire risk prediction.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Technology, Yibin University, Yibin, China",
    "School of Business English, Chengdu International Studies University (Yibin Campus), Yibin, China"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Bojie Chen"
    },
    {
      "affs": [
        1
      ],
      "name": "Anping Zeng"
    },
    {
      "name": "Yu Xie"
    },
    {
      "name": "Xinjie Wang"
    },
    {
      "name": "Lihong Zhu"
    },
    {
      "name": "Qina Xie"
    },
    {
      "name": "Guoqing Long"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/86q2xdkk31pht.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0355829",
  "fulltext_url": null,
  "id": "86q2xdkk31pht",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0355829",
  "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|Fire effects on ecosystems"
    ],
    [
      "plos",
      "/Biology and life sciences/Ecology/Forest ecology"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Earth sciences/Seasons"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Ecology/Forest ecology"
    ],
    [
      "plos",
      "/Ecology and environmental sciences/Wildfires"
    ],
    [
      "plos",
      "/Engineering and technology/Energy and power/Fuels"
    ],
    [
      "plos",
      "/Engineering and technology/Fire engineering"
    ],
    [
      "plos",
      "/Medicine and health sciences/Epidemiology/Medical risk factors"
    ],
    [
      "plos",
      "/Physical sciences/Materials science/Materials/Fuels"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ]
  ],
  "openalex": "W7204960499",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0355829&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/86q2xdkk31pht.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:02Z",
      "native_id": "10.1371/journal.pone.0355829",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0355829"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:ecology",
      "subfield": "ecology"
    },
    {
      "field": "chemistry",
      "origin": "plos:materials science",
      "subfield": "materials-polymers"
    },
    {
      "field": "medicine",
      "origin": "plos:epidemiology",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors",
  "title_text": "Physically aligned forest fire risk prediction: A deep learning framework coupling fuel and climate multivariate factors",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:02Z",
  "url": "https://research.draconitic.com/paper/86q2xdkk31pht",
  "venue": "PLOS One",
  "versions": []
}