Landslide identification using machine learning techniques: Review, motivation, and future prospects
Abstract The WHO (World Health Organization) study reports that, between 1998-2017, 4.8
million people have been affected by landslides with more than 18000 deaths. The …
million people have been affected by landslides with more than 18000 deaths. The …
[HTML][HTML] Machine-learning based landslide susceptibility modelling with emphasis on uncertainty analysis
Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation
strategies for future calamities. In this context, research on landslide susceptibility modelling …
strategies for future calamities. In this context, research on landslide susceptibility modelling …
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 …
Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques
This paper describes a novel methodology where Machine Learning Algorithms (MLAs)
have been integrated to assess the landslide risk for slow moving mass movements …
have been integrated to assess the landslide risk for slow moving mass movements …
GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach
The May 2nd and 3rd, 2023 landslide in Rwanda's Western Province caused a devastating
natural disaster, resulting in the tragic loss of 95 lives. Ngororero, Rubavu, Nyabihu, and …
natural disaster, resulting in the tragic loss of 95 lives. Ngororero, Rubavu, Nyabihu, and …
Exploring the uncertainty of landslide susceptibility assessment caused by the number of non–landslides
Q Liu, A Tang, D Huang - Catena, 2023 - Elsevier
Identifying the uncertainty caused by the number of non-landslides is necessary to obtain a
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …
Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms
Z Chen, D Song - International Journal of Digital Earth, 2023 - Taylor & Francis
Landslides are one of the most common geological hazards worldwide, especially in
Sichuan Province (Southwest China). The current study's main purposes are to explore the …
Sichuan Province (Southwest China). The current study's main purposes are to explore the …
Landslide and wildfire susceptibility assessment in Southeast Asia using ensemble machine learning methods
Q He, Z Jiang, M Wang, K Liu - Remote Sensing, 2021 - mdpi.com
Southeast Asia (SEA) is a region affected by landslide and wildfire; however, few studies on
susceptibility modeling for the two hazards together have been conducted for this region …
susceptibility modeling for the two hazards together have been conducted for this region …
An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India
This study examined landslide susceptibility, an increasingly common problem in
mountainous regions across the world as a result of urbanization, deforestation, and various …
mountainous regions across the world as a result of urbanization, deforestation, and various …
Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China
J Cao, Z Zhang, J Du, L Zhang, Y Song, G Sun - Natural Hazards, 2020 - Springer
Jiuzhaigou, located in the transitional area between the Qinghai–Tibet Plateau and the
Sichuan Basin, is highly prone to geological hazards (eg, rock fall, landslide, and debris …
Sichuan Basin, is highly prone to geological hazards (eg, rock fall, landslide, and debris …