Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India
- 1Department of Civil Engineering, Jorhat Engineering College, Assam Science and Technology University
- 2Department of Civil Engineering, Jorhat Engineering College
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.
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BibTeX
@article{adhyapok2026landslide,
title = {{Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India}},
author = {Mrinaljyoti Adhyapok and Mrinal Kumar Dutta},
journal = {Discover Geoscience},
year = {2026},
month = oct,
doi = {10.1007/s44288-026-00781-2},
url = {https://doi.org/10.1007/s44288-026-00781-2},
note = {CC BY},
}
RIS
TY - JOUR
TI - Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India
AU - Mrinaljyoti Adhyapok
AU - Mrinal Kumar Dutta
PY - 2026
DA - 2026/10/25
JO - Discover Geoscience
DO - 10.1007/s44288-026-00781-2
UR - https://doi.org/10.1007/s44288-026-00781-2
AB - 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.
SN - 2948-1589
ER -