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 …
Particle swarm optimization variants for solving geotechnical problems: review and comparative analysis
Optimization techniques have drawn much attention for solving geotechnical engineering
problems in recent years. Particle swarm optimization (PSO) is one of the most widely used …
problems in recent years. Particle swarm optimization (PSO) is one of the most widely used …
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
EK Sahin - SN Applied Sciences, 2020 - Springer
Decision tree-based classifier ensemble methods are a machine learning (ML) technique
that combines several tree models to produce an effective or optimum predictive model, and …
that combines several tree models to produce an effective or optimum predictive model, and …
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 …
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 …
Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector …
Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices,
and can cause social upheaval and loss of life. As a result, many scientists study the …
and can cause social upheaval and loss of life. As a result, many scientists study the …
[HTML][HTML] Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer
As threats of landslide hazards have become gradually more severe in recent decades,
studies on landslide prevention and mitigation have attracted widespread attention in …
studies on landslide prevention and mitigation have attracted widespread attention in …
[HTML][HTML] Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm
Flash floods are responsible for loss of life and considerable property damage in many
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …
countries. Flood susceptibility maps contribute to flood risk reduction in areas that are prone …
[HTML][HTML] Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India
K Mandal, S Saha, S Mandal - Geoscience Frontiers, 2021 - Elsevier
Landslide is considered as one of the most severe threats to human life and property in the
hilly areas of the world. The number of landslides and the level of damage across the globe …
hilly areas of the world. The number of landslides and the level of damage across the globe …
Soft computing ensemble models based on logistic regression for groundwater potential mapping
Groundwater potential maps are one of the most important tools for the management of
groundwater storage resources. In this study, we proposed four ensemble soft computing …
groundwater storage resources. In this study, we proposed four ensemble soft computing …