A comprehensive review of machine learning‐based methods in landslide susceptibility map**

S Liu, L Wang, W Zhang, Y He, S Pijush - Geological Journal, 2023 - Wiley Online Library
Landslide susceptibility map** (LSM) has been widely used as an important reference for
development and construction planning to mitigate the potential social‐eco impact caused …

Modelling landslide susceptibility prediction: a review and construction of semi-supervised imbalanced theory

F Huang, H ** in a Mediterranean area
M Mohajane, R Costache, F Karimi, QB Pham… - Ecological …, 2021 - Elsevier
Forest fire disaster is currently the subject of intense research worldwide. The development
of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous …

[HTML][HTML] Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models

Y Wei, H Qiu, Z Liu, W Huangfu, Y Zhu, Y Liu… - Geoscience …, 2024 - Elsevier
Landslide susceptibility assessment is crucial in predicting landslide occurrence and
potential risks. However, traditional methods usually emphasize on larger regions of …

Landslide Susceptibility map** using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

W Zhang, Y He, L Wang, S Liu, X Meng - Geological Journal, 2023 - Wiley Online Library
Landslide susceptibility analysis can provide theoretical support for landslide risk
management. However, some susceptibility analyses are not sufficiently interpretable …

Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils

L Wang, C Wu, Z Yang, L Wang - Computers and Geotechnics, 2023 - Elsevier
Abstract The Three Gorges Reservoir Area (TGRA) is one of the most important landslide-
prone regions in China, and rational stability evaluation of reservoir slopes in it is of great …

Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model

F Huang, Z Cao, SH Jiang, C Zhou, J Huang, Z Guo - Landslides, 2020 - Springer
Conventional supervised and unsupervised machine learning models used for landslide
susceptibility prediction (LSP) have many drawbacks, such as an insufficient number of …

[HTML][HTML] Landslide susceptibility zonation method based on C5. 0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Z Guo, Y Shi, F Huang, X Fan, J Huang - Geoscience Frontiers, 2021 - Elsevier
Abstract Machine learning algorithms are an important measure with which to perform
landslide susceptibility assessments, but most studies use GIS-based classification methods …

[HTML][HTML] Ensemble learning framework for landslide susceptibility map**: Different basic classifier and ensemble strategy

T Zeng, L Wu, D Peduto, T Glade, YS Hayakawa… - Geoscience …, 2023 - Elsevier
The application of ensemble learning models has been continuously improved in recent
landslide susceptibility research, but most studies have no unified ensemble framework …