W Chen, S Zhang, R Li, H Shahabi - Science of the total environment, 2018 - Elsevier
The main aim of the present study is to explore and compare three state-of-the art data mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide …
This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements …
Landslide inventories are in high demand for risk assessment of this natural hazard, particularly in tropical mountainous regions. This research designed residual networks for …
Continuous geodetic measurements in landslide prone regions are necessary to avoid disasters and better understand the spatiotemporal and kinematic evolution of landslides …
The present paper analyzes potential and limitations of multi-temporal Differential Synthetic Aperture Radar Interferometry (DInSAR) techniques applied for the structural monitoring and …
Interferometric processing of series of data acquired over time by synthetic aperture radar (SAR) satellites makes it possible to measure millimetric deformations (typically due to …
The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of entropy …
VK Pandey, HR Pourghasemi… - Geocarto International, 2020 - Taylor & Francis
The main objective of this study to produce landslide susceptibility zones using maximum entropy (MaxEnt) and support vector machine (SVM) data-driven models along the Tipari to …