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March 15, 2026Transactions in GIS

Enhancing Landslide Susceptibility and Dynamic Exposure Assessment Using Interpretable Machine Learning: A Case Study of the Qinba Mountain Area, China

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Authors

YSYi ShiQLQigen LinHHHaoyuan Hong

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Overview

This case study demonstrates machine learning improves landslide susceptibility mapping, suggesting effective urban planning and early warning systems are essential for high-risk areas.

Key Points

  • The aim is to enhance landslide susceptibility assessments and dynamic exposure evaluations using machine learning methods.
  • Utilized four machine learning methods: Random Forest, Generalized Additive Model, Support Vector Machine, and Multivariate Adaptive Regression Splines.
  • Applied 18 conditioning factors to create landslide susceptibility models for the Qinba Mountain area.
  • Integrated time-series data on GDP and population grids to analyze exposure dynamics.
  • Random Forest achieved the highest predictive accuracy (AUC = 0.815) among models tested.
  • High-risk zones are identified in mountainous terrains, particularly around Nanyang, Hanzhong, and Ankang.
  • Integration of urbanization data shows increased exposure in high-susceptibility zones, indicating a need for improved planning.

Cite This Study

Shi et al. (2026) studied this question.

synapsesocial.com/papers/69b5ff3b83145bc643d1b618https://doi.org/10.1111/tgis.70228
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Landslide Susceptibility Prediction and Driving Force Analysis Integrating Machine Learning and Spatial Factor Optimization: A Case Study in the Guanyinyan Hydropower Station Reservoir Area2025
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  3. 3Comparative Landslide Susceptibility Mapping in Longchuan, Guangdong Province, China, Using Explainable Machine Learning2026
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