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A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

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Ghorbanzadeh, O, Blaschke, T, Aryal, J ORCID: 0000-0002-4875-2127 and Gholaminia, K 2018 , 'A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping' , Journal of Spatial Science , pp. 1-18 , doi: 10.1080/14498596.2018.1505564.

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Abstract

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%.

Item Type: Article
Authors/Creators:Ghorbanzadeh, O and Blaschke, T and Aryal, J and Gholaminia, K
Keywords: sentinel-1, land subsidence, adaptive neuro-fuzzy inference system
Journal or Publication Title: Journal of Spatial Science
Publisher: Taylor & Francis Asia Pacific
ISSN: 1449-8596
DOI / ID Number: 10.1080/14498596.2018.1505564
Copyright Information:

Copyright 2018 the authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/

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