Landslide prediction, monitoring and early warning: a concise review of state-of-the-art

BG Chae, HJ Park, F Catani, A Simoni, M Berti - Geosciences Journal, 2017 - Springer
Landslide is one of the repeated geological hazards during rainy season, which causes
fatalities, damage to property and economic losses in Korea. Landslides are responsible for …

Smoothed particle hydrodynamics method for fluid flows, towards industrial applications: Motivations, current state, and challenges

MS Shadloo, G Oger, D Le Touzé - Computers & Fluids, 2016 - Elsevier
Abstract Smoothed Particle Hydrodynamics (SPH) is a relatively new meshless numerical
approach which has attracted significant attention in the last two decades. Compared with …

Recommendations for the quantitative analysis of landslide risk

J Corominas, C van Westen, P Frattini… - Bulletin of engineering …, 2014 - Springer
This paper presents recommended methodologies for the quantitative analysis of landslide
hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and …

r. avaflow v1, an advanced open-source computational framework for the propagation and interaction of two-phase mass flows

M Mergili, JT Fischer, J Krenn… - Geoscientific Model …, 2017 - gmd.copernicus.org
r. avaflow represents an innovative open-source computational tool for routing rapid mass
flows, avalanches, or process chains from a defined release area down an arbitrary …

Smoothed particle hydrodynamics (SPH): an overview and recent developments

MB Liu, GR Liu - Archives of computational methods in engineering, 2010 - Springer
Smoothed particle hydrodynamics (SPH) is a meshfree particle method based on
Lagrangian formulation, and has been widely applied to different areas in engineering and …

A depth-averaged debris-flow model that includes the effects of evolving dilatancy. I. Physical basis

RM Iverson, DL George - Proceedings of the Royal …, 2014 - royalsocietypublishing.org
To simulate debris-flow behaviour from initiation to deposition, we derive a depth-averaged,
two-phase model that combines concepts of critical-state soil mechanics, grain-flow …

Application of ensemble-based machine learning models to landslide susceptibility map**

PR Kadavi, CW Lee, S Lee - Remote Sensing, 2018 - mdpi.com
The main purpose of this study was to produce landslide susceptibility maps using various
ensemble-based machine learning models (ie, the AdaBoost, LogitBoost, Multiclass …

[HTML][HTML] Inundation, flow dynamics, and damage in the 9 January 2018 Montecito debris-flow event, California, USA: Opportunities and challenges for post-wildfire risk …

JW Kean, DM Staley, JT Lancaster… - …, 2019 - pubs.geoscienceworld.org
Shortly before the beginning of the 2017–2018 winter rainy season, one of the largest fires
in California (USA) history (Thomas fire) substantially increased the susceptibility of steep …

Guidelines for landslide susceptibility, hazard and risk zoning for land use planning

R Fell, J Corominas, C Bonnard, L Cascini, E Leroi… - Engineering …, 2008 - Elsevier
Landslide susceptibility and hazard zoning, and to a lesser extent, landslide risk zoning,
have experienced extensive development during the last few decades. Most of these zoning …

Novel GIS based machine learning algorithms for shallow landslide susceptibility map**

A Shirzadi, K Soliamani, M Habibnejhad, A Kavian… - Sensors, 2018 - mdpi.com
The main objective of this research was to introduce a novel machine learning algorithm of
alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation …