Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Landslides are one of the catastrophic natural hazards that occur in mountainous areas,
leading to loss of life, damage to properties, and economic disruption. Landslide …
leading to loss of life, damage to properties, and economic disruption. Landslide …
Rainfall induced landslide studies in Indian Himalayan region: a critical review
Landslides are one of the most devastating and recurring natural disasters and have
affected several mountainous regions across the globe. The Indian Himalayan region is no …
affected several mountainous regions across the globe. The Indian Himalayan region is no …
[HTML][HTML] Landslide susceptibility prediction using slope unit-based machine learning models considering the heterogeneity of conditioning factors
To perform landslide susceptibility prediction (LSP), it is important to select appropriate
mapping unit and landslide-related conditioning factors. The efficient and automatic multi …
mapping unit and landslide-related conditioning factors. The efficient and automatic multi …
Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous …
Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These
landslides can be deadly to the community living downslope with their fast pace, turning …
landslides can be deadly to the community living downslope with their fast pace, turning …
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
Commonly used data-driven models for landslide susceptibility prediction (LSP) can be
mainly classified as heuristic, general statistical or machine learning models. This study …
mainly classified as heuristic, general statistical or machine learning models. This study …
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
DT Bui, P Tsangaratos, VT Nguyen, N Van Liem… - Catena, 2020 - Elsevier
The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …
(DLNN) model in landslide susceptibility assessments and compare its predictive …
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
Landslides represent a part of the cascade of geological hazards in a wide range of geo-
environments. In this study, we aim to investigate and compare the performance of two state …
environments. In this study, we aim to investigate and compare the performance of two state …
A spatially explicit deep learning neural network model for the prediction of landslide susceptibility
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 …
bigger role in promoting our understanding of future landslides and their magnitude. This …
A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping
This study introduces four heterogeneous ensemble-learning techniques, that is, stacking,
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …
Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based …
Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we
propose a new flood susceptibility mapping technique. We employ new ensemble models …
propose a new flood susceptibility mapping technique. We employ new ensemble models …