{
  "abstract": "Accurate large-scale wildfire spread modelling requires models that capture both the environmental conditions associated with fire occurrence and the local dynamics of fire propagation. We propose a three-stage framework that combines a Random Forest (RF) model with a cellular automaton (CA). First, an RF model trained on the 2021 Canadian fire season estimates daily pixel-level fire-occurrence probabilities. Second, quantile gradient boosting models provide optional spread-rate priors for sensitivity analysis. Third, an RF-informed CA combines the RF probability layer with neighbourhood-driven spread on a 5 km grid. The RF model achieved AUC values of 0.725--0.795 on the 2022--2024 datasets, while the RF-informed CA achieved substantially higher spatial overlap than the evaluated CA-only baselines in the 2023 simulation. A higher-resolution simulation provides an additional qualitative assessment of local spatial errors. These results suggest that combining RF-derived probabilities with local CA spread can improve large-scale wildfire simulations under the tested conditions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.01675",
  "author_total": 3,
  "authors": [
    {
      "name": "Siyu Chen"
    },
    {
      "name": "Esha Saha"
    },
    {
      "name": "Hao Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/52scnecc0xgpk.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.01675",
  "fulltext_url": null,
  "id": "52scnecc0xgpk",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.01675",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.AP"
    ],
    [
      "openalex",
      "2306|Fire effects on ecosystems"
    ]
  ],
  "openalex": "W7207557073",
  "pdf_url": "https://arxiv.org/pdf/2609.01675",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/52scnecc0xgpk.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:26Z",
      "native_id": "2609.01675",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.01675"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.AP",
      "subfield": "statistics"
    }
  ],
  "title": "Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling",
  "title_text": "Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:26Z",
  "url": "https://research.draconitic.com/paper/52scnecc0xgpk",
  "venue": "arXiv",
  "versions": []
}