Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques
Every year man-made and natural disasters impact the lives of millions of people. The
frequency of occurrence of such disasters is steadily increasing since the last 50 years, and …
frequency of occurrence of such disasters is steadily increasing since the last 50 years, and …
The effects of vegetation traits and their stability functions in bio-engineered slopes: A perspective review
Bio-engineered slopes use vegetation as “live” protection elements against the triggering
forces of landslides, erosions and debris flows. In this paper, the effects of basic plant traits …
forces of landslides, erosions and debris flows. In this paper, the effects of basic plant traits …
A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility map**
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 …
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility map**
Landslides are regarded as one of the most common geological hazards in a wide range of
geo-environment. The aim of this study is to assess landslide susceptibility by integrating …
geo-environment. The aim of this study is to assess landslide susceptibility by integrating …
[HTML][HTML] Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks
In recent years, landslide susceptibility map** has substantially improved with advances
in machine learning. However, there are still challenges remain in landslide map** due to …
in machine learning. However, there are still challenges remain in landslide map** due to …
Machine learning ensemble modelling as a tool to improve landslide susceptibility map** reliability
Statistical landslide susceptibility map** 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 …
[HTML][HTML] Presenting logistic regression-based landslide susceptibility results
A new work-flow is proposed to unify the way the community shares Logistic Regression
results for landslide susceptibility purposes. Although Logistic Regression models and …
results for landslide susceptibility purposes. Although Logistic Regression models and …
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 …
Novel GIS based machine learning algorithms for shallow landslide susceptibility map**
The main objective of this research was to introduce a novel machine learning algorithm of
alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation …
alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation …
Optimization of computational intelligence models for landslide susceptibility evaluation
X Zhao, W Chen - Remote Sensing, 2020 - mdpi.com
This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide
disaster area. The evidential belief function (EBF)-based function tree (FT), logistic …
disaster area. The evidential belief function (EBF)-based function tree (FT), logistic …