[HTML][HTML] Parameters derived from and/or used with digital elevation models (DEMs) for landslide susceptibility map** and landslide risk assessment: a review

N Saleem, ME Huq, NYD Twumasi, A Javed… - … International Journal of …, 2019 - mdpi.com
Digital elevation models (DEMs) are considered an imperative tool for many 3D visualization
applications; however, for applications related to topography, they are exploited mostly as a …

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

J Huang, X Wu, S Ling, X Li, Y Wu, L Peng… - … Science and Pollution …, 2022 - Springer
To assess the status of hotspots and research trends on geographic information system
(GIS)–based landslide susceptibility (LS), we analysed 1142 articles from the Thomas …

The importance of input data on landslide susceptibility map**

K Gaidzik, MT Ramírez-Herrera - Scientific reports, 2021 - nature.com
Landslide detection and susceptibility map** are crucial in risk management and urban
planning. Constant advance in digital elevation models accuracy and availability, the …

[HTML][HTML] Landslide susceptibility map**: Machine and ensemble learning based on remote sensing big data

B Kalantar, N Ueda, V Saeidi, K Ahmadi, AA Halin… - Remote Sensing, 2020 - mdpi.com
Predicting landslide occurrences can be difficult. However, failure to do so can be
catastrophic, causing unwanted tragedies such as property damage, community …

GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models

W Chen, X ** integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model
F Yan, Q Zhang, S Ye, B Ren - Geomorphology, 2019 - Elsevier
Landslides, which could cause huge losses of lives or property damages, result from several
different environmental factors whose influences on landslides are very complex. Therefore …

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

DC Camilo, L Lombardo, PM Mai, J Dou… - … Modelling & Software, 2017 - Elsevier
Grid-based landslide susceptibility models at regional scales are computationally
demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based …

Landslide recognition and map** in a mixed forest environment from airborne LiDAR data

T Görüm - Engineering Geology, 2019 - Elsevier
A precise, accurate and complete landslide inventory is indispensable for the establishment
of reliable landslide susceptibility and hazard maps. In the preparation of landslide …

[HTML][HTML] Comparison of support vector machine, Bayesian logistic regression, and alternating decision tree algorithms for shallow landslide susceptibility map** …

VH Nhu, D Zandi, H Shahabi, K Chapi, A Shirzadi… - Applied Sciences, 2020 - mdpi.com
This paper aims to apply and compare the performance of the three machine learning
algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and …

Explainable boosting machines for slope failure spatial predictive modeling

AE Maxwell, M Sharma, KA Donaldson - Remote Sensing, 2021 - mdpi.com
Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest
neighbors (k NN), random forests (RF), support vector machines (SVM), and boosted …