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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 …
Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014
Sažetak Multiple criteria decision-making (MCDM) is considered as a complex decision
making (DM) tool involving both quantitative and qualitative factors. In recent years, several …
making (DM) tool involving both quantitative and qualitative factors. In recent years, several …
Predictive performances of ensemble machine learning algorithms in landslide susceptibility map** using random forest, extreme gradient boosting (XGBoost) and …
Across the globe, landslides have been recognized as one of the most detrimental
geological calamities, especially in hilly terrains. However, the correct determination of …
geological calamities, especially in hilly terrains. However, the correct determination of …
[HTML][HTML] Landslide susceptibility zonation method based on C5. 0 decision tree and K-means cluster algorithms to improve the efficiency of risk management
Abstract Machine learning algorithms are an important measure with which to perform
landslide susceptibility assessments, but most studies use GIS-based classification methods …
landslide susceptibility assessments, but most studies use GIS-based classification methods …
An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …
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 …
Assessing the predictive capability of ensemble tree methods for landslide susceptibility map** 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 comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
W Chen, X ** and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia
Simada area is found in the South Gondar Zone of Amhara National Regional State and it is
780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of …
780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of …
Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
E Dodangeh, B Choubin, AN Eigdir, N Nabipour… - Science of the Total …, 2020 - Elsevier
Flood susceptibility projections relying on standalone models, with one-time train-test data
splitting for model calibration, yields biased results. This study proposed novel integrative …
splitting for model calibration, yields biased results. This study proposed novel integrative …