{
  "abstract": "With the rapid growth of machine learning (ML) applications in landslide susceptibility mapping (LSM), many studies have relied on large and diverse datasets to improve predictive performance. However, in data-scarce regions, model transferability becomes challenging, and not all input variables contribute meaningful information to landslide prediction, leading to the well-known problem of “garbage in, garbage out”. Therefore, careful selection of physically meaningful predictors is essential. In this study, we develop a Random Forest–based framework for landslide mapping using topographic and climatic factors only as inputs. Notably, the model incorporates effective rainfall (ER), derived from a process-based hydrological model, and a recently proposed topographic metric, topographic source density (D_ts), that were not used in previous LSM studies. Results demonstrate that incorporating these predictors substantially improves model performance, achieving a receiver operating characteristic area under the curve (ROC-AUC) of 0.972. Overall, the findings suggest that a physically meaningful machine learning approach can reliably predict regional landslide susceptibility.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "name": "Saba Shakeel Raina"
    },
    {
      "name": "Basudev Biswal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ds69syhfqdvxq.bib",
  "cited_by": 0,
  "doi": "10.31223/x5hc0v",
  "fulltext_url": null,
  "id": "ds69syhfqdvxq",
  "issns": [],
  "landing_url": "https://eartharxiv.org/repository/view/15828/",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/legalcode",
  "metadata_licence": null,
  "native_subjects": [
    [
      "bepress",
      "Engineering"
    ],
    [
      "bepress",
      "Landslide susceptibility mapping; Machine learning; Random Forest; Effective rainfall; Topographic source density; Topographic controls; Climatic controls; Model interpretability; California."
    ]
  ],
  "openalex": null,
  "pdf_url": "https://eartharxiv.org/repository/object/15828/download/27419/",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ds69syhfqdvxq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:52Z",
      "native_id": "15828",
      "source": "eartharxiv",
      "url": "https://eartharxiv.org/repository/view/15828/"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "source:eartharxiv",
      "subfield": "general"
    }
  ],
  "title": "Role of Topo-Climatic Factors in Landslide Susceptibility Mapping: A Machine Learning Perspective",
  "title_text": "Role of Topo-Climatic Factors in Landslide Susceptibility Mapping: A Machine Learning Perspective",
  "type": "preprint",
  "updated_at": "2026-10-31T12:20:56Z",
  "url": "https://research.draconitic.com/paper/ds69syhfqdvxq",
  "venue": "EarthArXiv",
  "versions": []
}