{
  "abstract": "Landslide susceptibility mapping (LSM) along mountain highways in tectonically active Himalayan terrain requires methods that can represent non-linear interactions among terrain, geology, hydrology and anthropogenic controls. This study compares a multilayer perceptron artificial neural network (MLP-ANN), a Random Forest (RF) classifier and an expert-weighted analytical hierarchy process (AHP) for the 123 km NH-513 corridor between Pasighat and Yingkiong, Arunachal Pradesh, India. Twelve conditioning factors were prepared in a GIS environment and linked with a georeferenced inventory of 230 landslide initiation points and 230 non-landslide samples. Multicollinearity was screened using variance inflation factor analysis (all VIF < 5). Model performance was evaluated using a 70:30 train-test split, five-fold spatial block cross-validation (SBCV), ROC-AUC, accuracy, precision, recall, F1-score and Matthews correlation coefficient. The ANN achieved predictive AUC = 0.954, accuracy = 0.936 and MCC = 0.873, while RF achieved predictive AUC = 0.980, accuracy = 0.954 and MCC = 0.908. Conservative SBCV estimates were 0.921 ± 0.028 for ANN and 0.941 ± 0.022 for RF, indicating mild but transparent spatial autocorrelation effects. AHP showed limited discrimination (predictive AUC = 0.551). Bootstrap uncertainty analysis identified transitional and corridor-margin zones requiring field verification. Slope gradient was the dominant factor in both ML models, followed by LULC, geology and drainage density. The results provide a reproducible, uncertainty-aware framework for highway-slope screening in the Eastern Himalaya.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Civil Engineering, Jorhat Engineering College, Assam Science and Technology University",
    "Department of Civil Engineering, Jorhat Engineering College"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Mrinaljyoti Adhyapok"
    },
    {
      "affs": [
        1
      ],
      "name": "Mrinal Kumar Dutta"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ey8y52w64bw2w.bib",
  "cited_by": 0,
  "doi": "10.1007/s44288-026-00781-2",
  "fulltext_url": "https://doi.org/10.1007/s44288-026-00781-2",
  "id": "ey8y52w64bw2w",
  "issns": [
    "2948-1589"
  ],
  "landing_url": "https://doi.org/10.1007/s44288-026-00781-2",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC801-809"
    ],
    [
      "lcc",
      "QE1-996.5"
    ],
    [
      "openalex",
      "2308|Landslides and related hazards"
    ]
  ],
  "openalex": "W7223049727",
  "pdf_url": "https://link.springer.com/content/pdf/10.1007/s44288-026-00781-2.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-25",
  "publisher": "Springer",
  "ris": "https://research.draconitic.com/paper/ey8y52w64bw2w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:48Z",
      "native_id": "c3a627d869f042a483027f392d483d3e",
      "source": "doaj",
      "url": "https://doi.org/10.1007/s44288-026-00781-2"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2308|Landslides and related hazards",
      "subfield": "environmental-science"
    }
  ],
  "title": "Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India",
  "title_text": "Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:48Z",
  "url": "https://research.draconitic.com/paper/ey8y52w64bw2w",
  "venue": "Discover Geoscience",
  "versions": []
}