A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. Issue 3 (1st September 2020)

• Record Type: Journal Article
• Authors: Ghorbanzadeh, Omid; Blaschke, Thomas; Aryal, Jagannath; Gholaminia, Khalil
• Is Part Of: Journal of spatial science. Volume 65:Issue 3(2020)
• Page Start: 401
• Page End: 418
• Publication Date: 2020-09-01
• Subjects: Sentinel-1 -- land subsidence -- adaptive neuro-fuzzy inference system -- Amol County -- Iran; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2018.1505564
• Languages: English
• ISSNs: 1449-8596

Abstract

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%.