Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE

Samy Ismail Elmahdy, Mohamed Mostafa Mohamed, Tarig A. Ali, Jamal El Din Abdalla, Mohamed Abouleish

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)315-331
Number of pages17
JournalGeocarto International
Volume37
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • Al Ain
  • UAE
  • land subsidence
  • random forest
  • sinkholes

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Water Science and Technology

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