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[HTML][HTML] A review of ensemble learning algorithms used in remote sensing applications
Y Zhang, J Liu, W Shen - Applied Sciences, 2022 - mdpi.com
Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …
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 …
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 …
Modelling landslide susceptibility prediction: a review and construction of semi-supervised imbalanced theory
F Huang, H **/links/62541c01ef013420666a60a7/Comparisons-of-heuristic-general-statistical-and-machine-learning-models-for-landslide-susceptibility-prediction-and-map**.pdf" data-clk="hl=th&sa=T&oi=gga&ct=gga&cd=4&d=4629949251791971158&ei=TtTCZ53vFoa56rQPpO3LmAY" data-clk-atid="VmMlYVDiQEAJ" target="_blank">[PDF] researchgate.net
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and map**
Commonly used data-driven models for landslide susceptibility prediction (LSP) can be
mainly classified as heuristic, general statistical or machine learning models. This study …
mainly classified as heuristic, general statistical or machine learning models. This study …
[HTML][HTML] Landslide susceptibility map** using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia
AM Youssef, HR Pourghasemi - Geoscience Frontiers, 2021 - Elsevier
The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …
Flood susceptibility map** with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory
Floods are one of the most widespread natural hazards occurring across the globe. The
main objective of this study was to produce flood susceptibility maps for the province of …
main objective of this study was to produce flood susceptibility maps for the province of …
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 …
A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
The environmental factors of landslide susceptibility are generally uncorrelated or non-
linearly correlated, resulting in the limited prediction performances of conventional machine …
linearly correlated, resulting in the limited prediction performances of conventional machine …
GIS-based comparative assessment of flood susceptibility map** using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and …
Flood is a devastating natural hazard that may cause damage to the environment
infrastructure, and society. Hence, identifying the susceptible areas to flood is an important …
infrastructure, and society. Hence, identifying the susceptible areas to flood is an important …