Landslide Susceptibility Assessment in Granite Areas Considering Factor Combination Mining and Intelligent Algorithms

FANG  Qiong; KONG Yaqian; WANG  Can; LI  Jiaying; DONG  Hui

doi:10.6180/jase.202606_29(6).0022

Landslide Susceptibility Assessment in Granite Areas Considering Factor Combination Mining and Intelligent Algorithms

Civil Engineering Environmental Engineering

FANG Qiong¹, KONG Yaqian¹, WANG Can², LI Jiaying³ , and DONG Hui³

¹Hunan Natural Resources Service Center, Changsha 410004, China

²Hunan Institute of Geological Disaster Investigation and Monitoring, Changsha 410004, China

³College of Civil Engineering, Xiangtan University, Xiangtan 411105, China

Received: June 25, 2025
Accepted: November 2, 2025
Publication Date: November 22, 2025

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202606_29(6).0022

This study focuses on the granite distribution area in Hunan Province as the study area. A total of 1684 landslides within the area are investigated and statistically analyzed. Dominant influencing factors are selected based on correlation and redundancy analyses. The importance of these dominant factors is ranked using the random forest (RF) model. The Frequent Pattern growth algorithm is employed to mine frequent factor combinations associated with landslides. Landslide susceptibility is then evaluated using multiple intelligent algorithms, namely the logistic regression, RF, support vector machine, deep belief network (DBN), and deep neural network, and the results are validated. The association rules between landslide susceptibility and the dominant factors and factor combinations are investigated. The results indicate that the selected dominant factors, ranked by importance, are: annual average rainfall, land use type, surface roughness, geological age, vegetation type, slope, soil erosion modulus, distance from water systems, soil type, distance from highways. Seven frequent factor combinations for landslides are identified, and areas possessing these combinations exhibit a higher likelihood of landslide occurrence. The landslide susceptibility assessment results based on all five intelligent models are accurate. Among them, the DBN model demonstrates the best performance. Significant association rules existing between landslide susceptibility and the factors and factor combinations are explored.

Keywords: Landslide susceptibility assessment; factor combination mining; granite characteristics; association rule algorithm; deep belief network

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