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 ** using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
AM Youssef, HR Pourghasemi - Geoscience Frontiers, 2021 - Elsevier
The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …

[HTML][HTML] A review of statistically-based landslide susceptibility models

P Reichenbach, M Rossi, BD Malamud, M Mihir… - Earth-science …, 2018 - Elsevier
In this paper, we do a critical review of statistical methods for landslide susceptibility
modelling and associated terrain zonations. Landslide susceptibility is the likelihood of a …

[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 …

Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility map**

Y Wu, Y Ke, Z Chen, S Liang, H Zhao, H Hong - Catena, 2020 - Elsevier
Landslides are a common type of natural disaster that brings great threats to the human lives
and economic development around the world, especially in the Chinese Loess Plateau …

Landslide4sense: Reference benchmark data and deep learning models for landslide detection

O Ghorbanzadeh, Y Xu, P Ghamisi, M Kopp… - arxiv preprint arxiv …, 2022 - arxiv.org
This study introduces\textit {Landslide4Sense}, a reference benchmark for landslide
detection from remote sensing. The repository features 3,799 image patches fusing optical …

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

D Van Dao, A Jaafari, M Bayat, D Mafi-Gholami, C Qi… - Catena, 2020 - Elsevier
With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …

[HTML][HTML] Predicting flood susceptibility using LSTM neural networks

Z Fang, Y Wang, L Peng, H Hong - Journal of Hydrology, 2021 - Elsevier
Identifying floods and producing flood susceptibility maps are crucial steps for decision-
makers to prevent and manage disasters. Plenty of studies have used machine learning …

AI-powered landslide susceptibility assessment in Hong Kong

H Wang, L Zhang, H Luo, J He, RWM Cheung - Engineering Geology, 2021 - Elsevier
Landslide susceptibility assessment is essential for regional landslide risk assessment and
mitigation. Most past studies involved cell-based analysis that takes landslide incidents as …