An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27350%2F13%3A86088911" target="_blank" >RIV/61989100:27350/13:86088911 - isvavai.cz</a>

Výsledek na webu

<a href="http://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=3&SID=Q1w2CFbxYOGhCvgPPkg&page=1&doc=4" target="_blank" >http://apps.webofknowledge.com/full_record.do?product=UA&search_mode=GeneralSearch&qid=3&SID=Q1w2CFbxYOGhCvgPPkg&page=1&doc=4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12040-013-0281-3" target="_blank" >10.1007/s12040-013-0281-3</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

Popis výsledku v původním jazyce

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However,

Název v anglickém jazyce

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

Popis výsledku anglicky

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However,

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JN - Stavebnictví

OECD FORD obor

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Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Journal of Earth System Science

ISSN

0253-4126

e-ISSN

—

Svazek periodika

122

Číslo periodika v rámci svazku

APR 2013

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

18

Strana od-do

371-388

Kód UT WoS článku

000317606500008

EID výsledku v databázi Scopus

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