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dc.contributor.authorYilmaz, Işık
dc.contributor.authorMarschalko, Marian
dc.contributor.authorBednárik, Martin
dc.date.accessioned2013-06-05T12:25:42Z
dc.date.available2013-06-05T12:25:42Z
dc.date.issued2013
dc.identifier.citationJournal of Earth System Science. 2013, vol. 122, issue 2, p. 371-388.cs
dc.identifier.issn0253-4126
dc.identifier.issn0973-774X
dc.identifier.urihttp://hdl.handle.net/10084/96386
dc.description.abstractThe 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, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.cs
dc.language.isoencs
dc.publisherSpringercs
dc.relation.ispartofseriesJournal of Earth System Sciencecs
dc.relation.urihttp://dx.doi.org/10.1007/s12040-013-0281-3cs
dc.subjectcollapse susceptibility mapcs
dc.subjectgypsumcs
dc.subjectGIScs
dc.subjectbivariate (conditional probability)cs
dc.subjectmultivariate (logistic regression)cs
dc.subjectsoft computing (artificial neural networks)cs
dc.titleAn assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environcs
dc.typearticlecs
dc.identifier.locationNení ve fondu ÚKcs
dc.identifier.doi10.1007/s12040-013-0281-3
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume122cs
dc.description.issue2cs
dc.description.lastpage388cs
dc.description.firstpage371cs
dc.identifier.wos000317606500008


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