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


Ghorbanzadeh, O and Rostamzadeh, H and Blaschke, T and Gholaminia, K and Aryal, J, A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping, Journal of Spatial Science, 94, (2) pp. 1-18. ISSN 1449-8596 (2018) [Refereed Article]


Copyright Statement

Copyright 2018 the authors. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

DOI: doi:10.1080/14498596.2018.1505564


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 Details

Item Type:Refereed Article
Keywords:land subsidence susceptibility mapping, sentinel-1, land subsidence, adaptive neuro-fuzzy inference system, Amol County, Iran, Marand plain, k-fold cross-validation
Research Division:Engineering
Research Group:Geomatic engineering
Research Field:Photogrammetry and remote sensing
Objective Division:Environmental Management
Objective Group:Other environmental management
Objective Field:Other environmental management not elsewhere classified
UTAS Author:Aryal, J (Dr Jagannath Aryal)
ID Code:128076
Year Published:2018
Deposited By:Geography and Spatial Science
Deposited On:2018-08-31
Last Modified:2022-08-16
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