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] Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble …
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 …
[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 …
Advances in machine learning modeling reviewing hybrid and ensemble methods
The conventional machine learning (ML) algorithms are continuously advancing and
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
[HTML][HTML] Landslide susceptibility mapping using different GIS-based bivariate models
Landslides are the most frequent phenomenon in the northern part of Iran, which cause
considerable financial and life damages every year. One of the most widely used …
considerable financial and life damages every year. One of the most widely used …
A novel CNN-LSTM-based approach to predict urban expansion
Time-series remote sensing data offer a rich source of information that can be used in a wide
range of applications, from monitoring changes in land cover to surveillance of crops …
range of applications, from monitoring changes in land cover to surveillance of crops …
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] GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran
X Lei, W Chen, M Avand, S Janizadeh, N Kariminejad… - Remote Sensing, 2020 - mdpi.com
In the present study, gully erosion susceptibility was evaluated for the area of the Robat Turk
Watershed in Iran. The assessment of gully erosion susceptibility was performed using four …
Watershed in Iran. The assessment of gully erosion susceptibility was performed using four …
Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various …
Landslides are natural and sometimes quasi-natural hazards that are destructive to natural
resources and cause loss of human life every year. Hence, preparing susceptibility maps for …
resources and cause loss of human life every year. Hence, preparing susceptibility maps for …
[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 …