Review on remote sensing methods for landslide detection using machine and deep learning
Landslide, one of the most critical natural hazards, is caused due to specific compositional
slope movement. In the past decades, due to inflation of urbanized area and climate change …
slope movement. In the past decades, due to inflation of urbanized area and climate change …
Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
The main objective of this study is to evaluate and compare the performance of different
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme …
Bagging-based machine learning algorithms for landslide susceptibility modeling
T Zhang, Q Fu, H Wang, F Liu, H Wang, L Han - Natural hazards, 2022 - Springer
Landslide hazards have attracted increasing public attention over the past decades due to a
series of catastrophic consequences of landslide occurrence. Thus, the mitigation and …
series of catastrophic consequences of landslide occurrence. Thus, the mitigation and …
[HTML][HTML] Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and …
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] Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
[HTML][HTML] 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 …
Modeling landslide susceptibility using LogitBoost alternating decision trees and forest by penalizing attributes with the bagging ensemble
The major target of this study is to design two novel hybrid integration artificial intelligent
models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide …
models, which are denoted as LADT-Bagging and FPA-Bagging, for modeling landslide …
[HTML][HTML] Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …
ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands …