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Title
A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping
AuthorGhorbanzadeh Khalil Gholaminia, Omid ; Blaschke, Thomas ; Aryal, Jagannath ; Gholaminia, Khalil
Published in
Journal of Spatial Science, London, 2018, Vol. 2018, page 1-17
PublishedLondon : Taylor & Francis, 2018
LanguageEnglish
Document typeJournal Article
Keywords (EN)Sentinel-1 / land subsidence / adaptive neuro-fuzzy inference system / Amol County / Iran
ISSN1836-5655
URNurn:nbn:at:at-ubs:3-10038 Persistent Identifier (URN)
DOI10.1080/14498596.2018.1505564 
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 The work is publicly available
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A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping [3.04 mb]
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Abstract (English)

In this study, we evaluated the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) with six different membership functions (MFs). Using a geographic information system (GIS), we applied ANFIS to land subsidence susceptibility mapping (LSSM) in the study area of Amol County, northern Iran. As a novelty, we derived a land subsidence inventory from the differential synthetic aperture radar interferometry (DInSAR) of two Sentinel-1 images. We used 70% of surface subsidence deformation areas for training, while 30% were reserved for testing and validation. We then investigated regions that are susceptible to subsidence via the ANFIS method and evaluated the resulting prediction maps using receiver operating characteristics (ROC) curves. Out of the six different versions, the most accurate map was generated with a Gaussian membership function, yielding an accuracy of 84%.

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CC-BY-License (4.0)Creative Commons Attribution 4.0 International License