Results 41 to 50 of about 26,434 (253)

Technical Note: Assessing predictive capacity and conditional independence of landslide predisposing factors for shallow landslide susceptibility models [PDF]

open access: yes, 2012
The aim of this study is to identify the landslide predisposing factors' combination using a bivariate statistical model that best predicts landslide susceptibility.
C. Bateira, J. L. Zêzere, S. Pereira
core   +2 more sources

Riverside Landslide Susceptibility Overview

open access: yesWater, 2023
Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), whichaim to address global challenges, professionals in the field have developed diverse methodologiesto analyze, assess, and predict the occurrence of landslides ...
Nanehkaran A., Yaser   +6 more
openaire   +1 more source

Landslide Susceptibility Analysis in Samanlı Mountains Massif by Frequency Ratio and Artificial Neural Networks Methods

open access: yesDoğal Afetler ve Çevre Dergisi
Susceptibility analyses of landslides, which cause loss of life and property, have a high frequency of occurrence and are affected by many factors, play an important role in the prediction of possible landslides with the help of Geographic Information ...
Murat Uzun
doaj   +1 more source

Landslide Susceptibility Mapping in the Vrancea-Buzău Seismic Region, Southeast Romania

open access: yesGeosciences, 2021
This study presents the results of a landslide susceptibility analysis applied to the Vrancea-Buzău seismogenic region in the Carpathian Mountains, Romania. The target area is affected by a large diversity of landslide processes.
Hasnaa Harmouzi   +3 more
doaj   +1 more source

Statistical and spatial analysis of landslide susceptibility maps with different classification systems [PDF]

open access: yes, 2016
The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6124-1A landslide susceptibility map is an essential tool for land-use spatial planning and management in mountain areas.
Amorim, Samuel   +2 more
core   +2 more sources

TEMPORAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT USING LANDSLIDE DENSITY TECHNIQUE

open access: yesGeological Behavior, 2017
En este estudio, se realizó una evaluación temporal de deslizamientos de tierra en un área propensa a deslizamientos de tierra a lo largo de la carretera Ranau-Tambunan en Sabah, Malasia. La evaluación se basó en deslizamientos de tierra interpretados a partir de fotografías aéreas de 1978 y 1994 y también de trabajos de campo que se realizaron en 2009
Norbert Simon   +2 more
openaire   +2 more sources

Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal) [PDF]

open access: yes, 2004
A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall.
A. B. Ferreira   +6 more
core   +2 more sources

Mapping landslide susceptibility at national scale by spatial multi-criteria evaluation

open access: yesGeomatics, Natural Hazards & Risk, 2021
The representation of terrain propensity to generate landslides, meaning the mapping of landslide susceptibility, represents a first step in the assessment of the risk induced by these geomorphological hazards.
Adrian Grozavu   +1 more
doaj   +1 more source

Subsurface structure affects landslide susceptibility [PDF]

open access: yesEos, Transactions American Geophysical Union, 2011
The likelihood of landslides on an exposed bedrock hill is dependent on the strength of the bedrock as well as the slope of the hill. In general, stronger rocks provide increased resilience against landslides and are capable of supporting steeper slopes.
openaire   +1 more source

Landslide susceptibility modeling by interpretable neural network

open access: yesCommunications Earth & Environment, 2023
AbstractLandslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute to slope stability. Artificial neural networks (ANN) have been shown to improve prediction accuracy but are largely uninterpretable.
K. Youssef   +3 more
openaire   +3 more sources

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