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Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques

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Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz 5166616471, East Azerbaijan, Iran
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Institute of Environment, University of Tabriz, Tabriz 5166616471, East Azerbaijan, Iran
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Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil 5618985991, Ardabil, Iran
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Department of Civil Engineering, University of Maragheh, Maragheh 5518183111, East Azerbaijan, Iran
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Department of Bioresource Engineering, McGill University, 21111 Lakeshore, Ste Anne de Bellevue, QC H9X 3V9, Canada
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Department of Geography & Environmental Studies, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
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Author to whom correspondence should be addressed.
Academic Editor: Aristotelis Mantoglou
Water 2021, 13(19), 2622; https://doi.org/10.3390/w13192622
Received: 13 August 2021 / Revised: 6 September 2021 / Accepted: 16 September 2021 / Published: 23 September 2021
Groundwater over-abstraction may cause land subsidence (LS), and the LS mapping suffers the subjectivity associated with expert judgment. The paper seeks to reduce the subjectivity associated with the hazard, vulnerability, and risk mapping by formulating an inclusive multiple modeling (IMM), which combines two common approaches of multi-criteria decision-making (MCDM) at Level 1 and artificial intelligence (AI) at Level 2. Fuzzy catastrophe scheme (FCS) is used as MCDM, and support vector machine (SVM) is employed as AI. The developed methodology is applied in Iran’s Tasuj plain, which has experienced groundwater depletion. The result highlights hotspots within the study area in terms of hazard, vulnerability, and risk. According to the receiver operating characteristic and the area under curve (AUC), significant signals are identified at both levels; however, IMM increases the modeling performance from Level 1 to Level 2, as a result of its multiple modeling capabilities. In addition, the AUC values indicate that LS in the study area is caused by intrinsic vulnerability rather than man-made hazards. Still, the hazard plays the triggering role in the risk realization. View Full-Text
Keywords: land subsidence; risk realization; hazard; vulnerability land subsidence; risk realization; hazard; vulnerability
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MDPI and ACS Style

Nadiri, A.A.; Moazamnia, M.; Sadeghfam, S.; Barzegar, R. Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques. Water 2021, 13, 2622. https://doi.org/10.3390/w13192622

AMA Style

Nadiri AA, Moazamnia M, Sadeghfam S, Barzegar R. Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques. Water. 2021; 13(19):2622. https://doi.org/10.3390/w13192622

Chicago/Turabian Style

Nadiri, Ata A., Marjan Moazamnia, Sina Sadeghfam, and Rahim Barzegar. 2021. "Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques" Water 13, no. 19: 2622. https://doi.org/10.3390/w13192622

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