A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F20%3A82242" target="_blank" >RIV/60460709:41330/20:82242 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007%2Fs12665-020-08953-0" target="_blank" >https://link.springer.com/article/10.1007%2Fs12665-020-08953-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12665-020-08953-0" target="_blank" >10.1007/s12665-020-08953-0</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods

Popis výsledku v původním jazyce

Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the

Název v anglickém jazyce

A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods

Popis výsledku anglicky

Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50704 - Environmental sciences (social aspects)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Environmental Earth Sciences

ISSN

1866-6280

e-ISSN

1866-6299

Svazek periodika

79

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

000536301000005

EID výsledku v databázi Scopus

2-s2.0-85084519612