Prediction of the landslide susceptibility: which algorithm, which precision?

HR Pourghasemi, O Rahmati - Catena, 2018 - Elsevier
Coupling machine learning algorithms with spatial analytical techniques for landslide
susceptibility modeling is a worth considering issue. So, the current research intend to …

[HTML][HTML] Landslide map** from multi-sensor data through improved change detection-based Markov random field

P Lu, Y Qin, Z Li, AC Mondini, N Casagli - Remote Sensing of Environment, 2019 - Elsevier
Accurate landslide inventory map** is essential for quantitative hazard and risk
assessment. Although multi-temporal change detection techniques have contributed greatly …

Comparison of four kernel functions used in support vector machines for landslide susceptibility map**: a case study at Suichuan area (China)

H Hong, B Pradhan, DT Bui, C Xu… - … , Natural Hazards and …, 2017 - Taylor & Francis
Suichuan is a mountainous area at the Jiangxi province in Central China, where rainfall-
induced landslides occur frequently. The purpose of this study is to assess landslide …

Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression

GF Lin, MJ Chang, YC Huang, JY Ho - Engineering Geology, 2017 - Elsevier
Quantitative landslide susceptibility assessment is necessary for mitigating casualties,
property damage, and economic loss. Identification of landslides and preparation of …

Derivation of long-term spatiotemporal landslide activity—A multi-sensor time series approach

R Behling, S Roessner, D Golovko… - Remote Sensing of …, 2016 - Elsevier
This paper presents a remote sensing-based method to efficiently derive multi-temporal
landslide inventories over large areas, which allows for the spatiotemporal analysis of …

[HTML][HTML] Automated spatiotemporal landslide map** over large areas using rapideye time series data

R Behling, S Roessner, H Kaufmann, B Kleinschmit - Remote Sensing, 2014 - mdpi.com
In the past, different approaches for automated landslide identification based on
multispectral satellite remote sensing were developed to focus on the analysis of the spatial …

[HTML][HTML] A meta-learning approach of optimisation for spatial prediction of landslides

B Pradhan, MI Sameen, HAH Al-Najjar, D Sheng… - Remote Sensing, 2021 - mdpi.com
Optimisation plays a key role in the application of machine learning in the spatial prediction
of landslides. The common practice in optimising landslide prediction models is to search for …

Characterising the spatial distribution, frequency and geomorphic controls on landslide occurrence, Molise, Italy

E Borgomeo, KV Hebditch, AC Whittaker, L Lonergan - Geomorphology, 2014 - Elsevier
An 815-km 2 area in Molise, central Italy, was used as a natural laboratory to characterise
the lithological, topographic, and fluvial controls on the spatial distribution and frequency of …

Rainfall thresholds for the activation of shallow landslides in the Italian Alps: the role of environmental conditioning factors

MR Palladino, A Viero, L Turconi, MT Brunetti… - Geomorphology, 2018 - Elsevier
The aim of the present work is to investigate the role exerted by selected environmental
factors in the activation of rainfall-triggered shallow landslides and to identify site-specific …