Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Hamidreza Gharechaee, Aliakbar Nazari Samani (), Shahram Khalighi Sigaroodi, Abolfazl Baloochiyan, Maryam Sadat Moosavi, Jason A. Hubbart and Seyed Mohammad Moein Sadeghi ()
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Hamidreza Gharechaee: Reclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj 3158577871, Iran
Aliakbar Nazari Samani: Reclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj 3158577871, Iran
Shahram Khalighi Sigaroodi: Reclamation of Arid and Mountainous Regions Department, Faculty of Natural Resources, University of Tehran, Karaj 3158577871, Iran
Abolfazl Baloochiyan: Faculty of Mechanical Engineering, Ozyegin University, Istanbul 34794, Turkey
Maryam Sadat Moosavi: Department of Arid and Desert Regions Management, School of Natural Resources & Desert Studies, Yazd University, Yazd 8915818411, Iran
Jason A. Hubbart: Division of Forestry and Natural Resources, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Percival Hall, Morgantown, WV 26506, USA
Seyed Mohammad Moein Sadeghi: School of Forest, Fisheries and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA

Land, 2023, vol. 12, issue 4, 1-20

Abstract: Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

Keywords: drylands; InSAR; machine learning; random forest; subsidence; susceptibility prediction (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:12:y:2023:i:4:p:843-:d:1117920

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