Fuzzy rule-based landslide susceptibility mapping in Yigilca Forest District (Northwest of Turkey)

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dc.contributor.authorAydın, Abdurrahim
dc.contributor.authorEker, Remzi
dc.date.accessioned2020-05-01T12:10:08Z
dc.date.available2020-05-01T12:10:08Z
dc.date.issued2016
dc.departmentDÜ, Orman Fakültesi, Orman Mühendisliği Bölümüen_US
dc.descriptionWOS: 000410445200015en_US
dc.description.abstractLandslide susceptibility map of Yigilca Forest District was formed based on developed fuzzy rules using GIS-based FuzzyCell software. An inventory of 315 landslides was updated through fieldworks after inventory map previously generated by the authors. Based on the landslide susceptibility mapping study previously made in the same area, for the comparison of two maps, same 8 landslide conditioning parameters were selected and then fuzzified for the landslide susceptibility mapping: land use, lithology, elevation, slope, aspect, distance to streams, distance to roads, and plan curvature. Mamdani model was selected as fuzzy inference system. After fuzzy rules definition, Center of Area (COA) was selected as defuzzification method in model. The output of developed model was normalized between 0 and 1, and then divided five classes such as very low, low, moderate, high, and very high. According to developed model based 8 conditioning parameters, landslide susceptibility in Yigilca Forest District varies between 32 and 67 (in range of 0-100) with 0.703 Area Under the Curve (AUC) value. According to classified landslide susceptibility map, in Yigilca Forest District, 32.89% of the total area has high and very high susceptibility while 29.59% of the area has low and very low susceptibility and the rest located in moderate susceptibility. The result of developed fuzzy rule based model compared with previously generated landslide map with logistic regression (LR). According to comparison of the results of two studies, higher differences exist in terms of AUC value and dispersion of susceptibility classes. This is because fuzzy rule based model completely depends on how parameters are classified and fuzzified and also depends on how truly the expert composed the rules. Even so, GIS-based fuzzy applications provide very valuable facilities for reasoning, which makes it possible to take into account inaccuracies and uncertainties.en_US
dc.description.sponsorshipDuzce University Research FundDuzce University [2013.2.2.180]en_US
dc.description.sponsorshipThis research was supported financially by Duzce University Research Fund (Grant Number: 2013.2.2.180). The authors would like to thank Yalcin Sefer and Ahmet Bora Kirklikci for the supports in fieldworks.en_US
dc.identifier.doi10.17099/jffiu.48480en_US
dc.identifier.endpage571en_US
dc.identifier.issn0535-8418
dc.identifier.issn1309-6257
dc.identifier.issue2en_US
dc.identifier.startpage559en_US
dc.identifier.urihttps://doi.org/10.17099/jffiu.48480
dc.identifier.urihttps://hdl.handle.net/20.500.12684/6036
dc.identifier.volume66en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIstanbul Univ, Fac Forestryen_US
dc.relation.ispartofJournal Of The Faculty Of Forestry-Istanbul Universityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFISen_US
dc.subjectFuzzyCellen_US
dc.subjectGISen_US
dc.subjectlandslide susceptibility mappingen_US
dc.subjectYigilcaen_US
dc.titleFuzzy rule-based landslide susceptibility mapping in Yigilca Forest District (Northwest of Turkey)en_US
dc.typeArticleen_US

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