Landslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry

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dc.authoridAkcay, Ozgun/0000-0003-0474-7518
dc.contributor.authorAkcay, Ozgun
dc.date.accessioned2025-01-27T21:01:32Z
dc.date.available2025-01-27T21:01:32Z
dc.date.issued2015
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractUnmanned Aerial Systems (UAS) are now capable of gathering high-resolution data, therefore, landslides can be explored in detail at larger scales. In this research, 132 aerial photographs were captured, and 85,456 features were detected and matched automatically using UAS photogrammetry. The root mean square (RMS) values of the image coordinates of the Ground Control Points (GPCs) varied from 0.521 to 2.293 pixels, whereas maximum RMS values of automatically matched features was calculated as 2.921 pixels. Using the 3D point cloud, which was acquired by aerial photogrammetry, the raster datasets of the aspect, slope, and maximally stable extremal regions (MSER) detecting visual uniformity, were defined as three variables, in order to reason fissure structures on the landslide surface. In this research, an Adaptive Neuro Fuzzy Inference System (ANFIS) and a Logistic Regression (LR) were implemented using training datasets to infer fissure data appropriately. The accuracy of the predictive models was evaluated by drawing receiver operating characteristic (ROC) curves and by calculating the area under the ROC curve (AUC). The experiments exposed that high-resolution imagery is an indispensable data source to model and validate landslide fissures appropriately.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [112Y336]
dc.description.sponsorshipThe author would like to extend special thanks to R. Cuneyt Erenoglu (Canakkale Onsekiz Mart University) for his efforts during fieldwork and data collection. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) (Project no: 112Y336). The author would also like to thank the anonymous reviewers for their constructive comments and suggestions in terms of improving the quality of the manuscript.
dc.identifier.doi10.3390/ijgi4042131
dc.identifier.endpage2158
dc.identifier.issn2220-9964
dc.identifier.issue4
dc.identifier.scopus2-s2.0-84952802184
dc.identifier.scopusqualityQ1
dc.identifier.startpage2131
dc.identifier.urihttps://doi.org/10.3390/ijgi4042131
dc.identifier.urihttps://hdl.handle.net/20.500.12428/27085
dc.identifier.volume4
dc.identifier.wosWOS:000367723300019
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofIsprs International Journal of Geo-Information
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectphotogrammetry
dc.subjectfuzzy logic
dc.subjectlandslide
dc.subjectfissure
dc.subjectorthophotos
dc.subjectimage processing
dc.subjectUnmanned Aerial System (UAS)
dc.titleLandslide Fissure Inference Assessment by ANFIS and Logistic Regression Using UAS-Based Photogrammetry
dc.typeArticle

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