Machine learning for landslides prevention: a survey
Landslides are one of the most critical categories of natural disasters worldwide and induce
severely destructive outcomes to human life and the overall economic system. To reduce its …
severely destructive outcomes to human life and the overall economic system. To reduce its …
Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility
In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …
statistical or machine learning approaches, have become popular to estimate the relative …
[HTML][HTML] A hybrid ensemble-based deep-learning framework for landslide susceptibility mapping
Landslides are highly hazardous geological disasters that can potentially threaten the safety
of human life and property. As a result, landslide susceptibility mapping (LSM) plays an …
of human life and property. As a result, landslide susceptibility mapping (LSM) plays an …
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
The environmental factors of landslide susceptibility are generally uncorrelated or non-
linearly correlated, resulting in the limited prediction performances of conventional machine …
linearly correlated, resulting in the limited prediction performances of conventional machine …
GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods
X Chen, W Chen - Catena, 2021 - Elsevier
Globally, but especially in China, landslides are considered to be one of the most severe
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …
[HTML][HTML] Landslide susceptibility mapping 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) …
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …
Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
This study developed a deep learning based technique for the assessment of landslide
susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian …
susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian …
Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China
The preparation of a landslide susceptibility map is considered to be the first step for
landslide hazard mitigation and risk assessment. However, these maps are accepted as end …
landslide hazard mitigation and risk assessment. However, these maps are accepted as end …
Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China
Landslide is a common natural hazard and responsible for extensive damage and losses in
mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was …
mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was …
Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability
Statistical landslide susceptibility mapping is a topic in complete and constant evolution,
especially since the introduction of machine learning (ML) methods. A new methodological …
especially since the introduction of machine learning (ML) methods. A new methodological …