Landslide susceptibility (LS) assessment provides a relative estimate of landslide spatial occurrence based on local terrain conditions. A literature review revealed that LS evaluation has been performed in many study areas worldwide using different methods, model types, different partition of the territory (mapping units) and a large variety of geo-environmental data. Among the different methods, statistical models have been largely used to evaluate LS, but the minority of articles presents a complete and comprehensive LS assessment that includes model performance analysis, prediction skills evaluation, and estimation of the errors and uncertainty. <br><br> The aim of this paper is to describe LAND-SE (LANDslide Susceptibility Evaluation) ...
Landslide susceptibility models are important for public safety, but often rely on inaccessible or u...
Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landsl...
Highlights • We modeled landslide susceptibility with statistical and machine learning techniques...
LAND-SUITE software (LANDslide - SUsceptibility Inferential Tool Evaluator) integrates, extends and ...
In the past 50 years, a large variety of statisti- cally based models and methods for landslide sus...
Most epistemic uncertainty within data-driven landslide susceptibility assessment results from error...
Areas that have experienced landslide events in the past and the conditioning factors present at the...
Statistical and deterministic methods are widely used ingeographic information system based landslid...
This paper deals with the development of a technique for mapping landslide susceptibility using a ge...
Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and...
The purpose of this study is to prepare a susceptibility map in a landslide-prone area in Greece usi...
Despite the large number of recent advances and developments in landslide susceptibility mapping (LS...
In this paper, we do a critical review of statistical methods for landslide susceptibility modelling...
Statistical and deterministic methods are widely used in GIS-based landslide susceptibility mapping....
peer reviewedLandslide susceptibility maps can be a useful tool to support holistic urban planning i...
Landslide susceptibility models are important for public safety, but often rely on inaccessible or u...
Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landsl...
Highlights • We modeled landslide susceptibility with statistical and machine learning techniques...
LAND-SUITE software (LANDslide - SUsceptibility Inferential Tool Evaluator) integrates, extends and ...
In the past 50 years, a large variety of statisti- cally based models and methods for landslide sus...
Most epistemic uncertainty within data-driven landslide susceptibility assessment results from error...
Areas that have experienced landslide events in the past and the conditioning factors present at the...
Statistical and deterministic methods are widely used ingeographic information system based landslid...
This paper deals with the development of a technique for mapping landslide susceptibility using a ge...
Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and...
The purpose of this study is to prepare a susceptibility map in a landslide-prone area in Greece usi...
Despite the large number of recent advances and developments in landslide susceptibility mapping (LS...
In this paper, we do a critical review of statistical methods for landslide susceptibility modelling...
Statistical and deterministic methods are widely used in GIS-based landslide susceptibility mapping....
peer reviewedLandslide susceptibility maps can be a useful tool to support holistic urban planning i...
Landslide susceptibility models are important for public safety, but often rely on inaccessible or u...
Landslide susceptibility maps are helpful tools to identify areas potentially prone to future landsl...
Highlights • We modeled landslide susceptibility with statistical and machine learning techniques...