Comparison of the frequency ratio, index of entropy, and artificial neural networks methods for landslide susceptibility mapping: A case study in Pınarbaşı/Kastamonu (North of Turkey)

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dc.contributor.authorTasoglu, E.
dc.contributor.authorAbujayyab, S.K.M.
dc.date.accessioned2024-09-29T16:21:12Z
dc.date.available2024-09-29T16:21:12Z
dc.date.issued2021
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThe selection of a suitable method is crucial for landslide susceptibility mapping (LSM). The main objective of this article was to compare the index of entropy (IoE), frequency ratio (FR), and artificial neural network (ANN) methods utilized in LSM. Landslide conditioning factors such as slope, distance to roads, aspect, curvature, plan curvature, elevation, profile curvature, distance to streams, soil types, topographic wetness index (TWI), and lithology have been used to carry out the LSM of the Pınarbaşı district (Kastamonu-Turkey). All models were compared considering their prediction rates obtained using the Area Under Curve (AUC) method. The models were evaluated with a total of 1000 points, including landslide and non-landslide areas. The findings of this study show that the AUC accuracy of the FR, IoE and ANN models were 0.873, 0.869, and 0.962, correspondingly. The ANN model achieved the highest accuracy. The AUC of both the FR and IoE models showed reasonably good accuracy for producing a landslide susceptibility map. The FR and IoE methods are straightforward and easy to implement compared to ANNs. Therefore, both can be efficiently used for the LSM. © 2022 Elsevier Inc. All rights reserved.en_US
dc.identifier.doi10.1016/B978-0-323-89861-4.00042-7
dc.identifier.endpage508en_US
dc.identifier.isbn978-032389861-4
dc.identifier.isbn978-032388615-4
dc.identifier.scopus2-s2.0-85142499619en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage491en_US
dc.identifier.urihttps://doi.org/10.1016/B978-0-323-89861-4.00042-7
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9604
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofComputers in Earth and Environmental Sciences: Artificial Intelligence and Advanced Technologies in Hazards and Risk Managementen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectComparisonen_US
dc.subjectFrequency ratioen_US
dc.subjectIndex of entropyen_US
dc.subjectLandslide susceptibility mappingen_US
dc.subjectTurkeyen_US
dc.titleComparison of the frequency ratio, index of entropy, and artificial neural networks methods for landslide susceptibility mapping: A case study in Pınarbaşı/Kastamonu (North of Turkey)en_US
dc.typeBook Parten_US

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