Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE. Issue 1 (30th January 2022)

• Record Type: Journal Article
• Authors: Elmahdy, Samy Ismail; Mohamed, Mohamed Mostafa; Ali, Tarig A.; Abdalla, Jamal El-Din; Abouleish, Mohamed
• Is Part Of: Geocarto international. Volume 37:Issue 1(2022)
• Page Start: 315
• Page End: 331
• Publication Date: 2022-01-30
• Subjects: Al Ain -- land subsidence -- sinkholes -- random forest -- UAE; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2020.1716398
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: This paper presents an approach to susceptibility mapping land subsidenceand sinkholes in the Al Ain area, UAE. A frequency ratio model was utilized to spatially analyse the relationship between locations of land subsidence and sinkhole and conditioning factors (CFs) to land subsidence susceptibility map. The values of eight essential CFs were employed as inputs to a random forest (RF) model. The produced map was compared with land subsidence and sinkhole locations and verified using the receiver operating characteristics (ROC). The results indicated a positive relationship and showed that the area under the curve was 88.4%, for the RF model. Thus, application of the approach using different algorithms could improve the performance of the modelling and the accuracy of the produced maps. The results of this study not only permit a better understanding of the impact of human activity and excessive groundwater extraction on ground surface stability, but also assist in enhancing geohazard mitigation strategies.