Application of novel ensemble models to improve landslide susceptibility mapping reliability

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985891%3A_____%2F23%3A00573977" target="_blank" >RIV/67985891:_____/23:00573977 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11310/23:10468601

Výsledek na webu

<a href="https://doi.org/10.1007/s10064-023-03328-8" target="_blank" >https://doi.org/10.1007/s10064-023-03328-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10064-023-03328-8" target="_blank" >10.1007/s10064-023-03328-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Application of novel ensemble models to improve landslide susceptibility mapping reliability

Popis výsledku v původním jazyce

Most landslides in the Eastern Golestan province in Iran occur in the Doji watershed. Their number, however, lies at the lower limit for reliable statistical analyses. By selecting a statistical sample in an area with rather homogeneous conditions (thereby reducing the number of meaningful covariates), significant insights can nevertheless be obtained. We relied on an inventory of 145 landslides which discerns between types of movement and implemented six machine learning algorithms (Decorate, DE-REPTree, Random Subspace, RS-REPTree, Dagging, and DA-REPTree) to produce landslide susceptibility maps. This allowed us to evaluate the relative importance and the effect of covariates in the models and identify factors that are consistently associated with the presence of landslides. Our results demonstrate that, even for a small landslide inventory, reliable susceptibility maps can be produced for homogeneous landscapes. We discuss that our approach could be used to assess the reliability of statistical approaches at small scales, where a distinctive trigger is lacking.

Název v anglickém jazyce

Application of novel ensemble models to improve landslide susceptibility mapping reliability

Popis výsledku anglicky

Most landslides in the Eastern Golestan province in Iran occur in the Doji watershed. Their number, however, lies at the lower limit for reliable statistical analyses. By selecting a statistical sample in an area with rather homogeneous conditions (thereby reducing the number of meaningful covariates), significant insights can nevertheless be obtained. We relied on an inventory of 145 landslides which discerns between types of movement and implemented six machine learning algorithms (Decorate, DE-REPTree, Random Subspace, RS-REPTree, Dagging, and DA-REPTree) to produce landslide susceptibility maps. This allowed us to evaluate the relative importance and the effect of covariates in the models and identify factors that are consistently associated with the presence of landslides. Our results demonstrate that, even for a small landslide inventory, reliable susceptibility maps can be produced for homogeneous landscapes. We discuss that our approach could be used to assess the reliability of statistical approaches at small scales, where a distinctive trigger is lacking.

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

2023

Kód důvěrnosti údajů

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

Název periodika

Bulletin of Engineering Geology and the Environment

ISSN

1435-9529

e-ISSN

1435-9537

Svazek periodika

82

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

21

Strana od-do

309

Kód UT WoS článku

001027857000001

EID výsledku v databázi Scopus

2-s2.0-85165221970