A benchmark dataset and workflow for landslide susceptibility zonation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985891%3A_____%2F24%3A00599857" target="_blank" >RIV/67985891:_____/24:00599857 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/24:10486353

Výsledek na webu

<a href="https://doi.org/10.1016/j.earscirev.2024.104927" target="_blank" >https://doi.org/10.1016/j.earscirev.2024.104927</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.earscirev.2024.104927" target="_blank" >10.1016/j.earscirev.2024.104927</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A benchmark dataset and workflow for landslide susceptibility zonation

Popis výsledku v původním jazyce

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data.nContrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consistingnof 7360 slope units encompassing an area of about 4, 100 km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models,(2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community.

Název v anglickém jazyce

A benchmark dataset and workflow for landslide susceptibility zonation

Popis výsledku anglicky

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data.nContrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consistingnof 7360 slope units encompassing an area of about 4, 100 km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models,(2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10505 - Geology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Earth-Science Reviews

ISSN

0012-8252

e-ISSN

1872-6828

Svazek periodika

258

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

26

Strana od-do

104927

Kód UT WoS článku

001334306900001

EID výsledku v databázi Scopus

2-s2.0-85204051068