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Ghoushchi SJ, Vahabzadeh S, Pamucar D. Applying hesitant q-rung orthopair fuzzy sets to evaluate uncertainty in subsidence causes factors. Heliyon 2024; 10:e29415. [PMID: 38681633 PMCID: PMC11046116 DOI: 10.1016/j.heliyon.2024.e29415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/07/2024] [Accepted: 04/08/2024] [Indexed: 05/01/2024] Open
Abstract
Land subsidence is a widespread problem impacting communities worldwide. Understanding the causes and factors of land subsidence is crucial for identifying and prioritizing effective mitigation measures. One of the main reasons for prioritizing land subsidence causes is the potential impact on infrastructure and the environment. The main objective of this paper is to emphasize the importance of prioritizing the causes of land subsidence. By understanding and prioritizing the factors contributing to land subsidence based on their impact and urgency, the aim is to develop targeted strategies for mitigation, inform policy decisions, and prevent further exacerbation of this problems. The study comprises three phases, where experts in the field provide their opinions and propose a robust hybrid framework. This framework integrates the Failure Mode and Effect Analysis (FMEA) and Step-wise Weight Assessment Ratio Analysis (SWARA) with Hesitant q-rung orthopair fuzzy set (Hq-ROFS). The performance of the proposed technique was then compared with two other decision-making techniques for evaluating and ranking land subsidence causes. According to the results, extraction of groundwater, excessive irrigation using groundwater, and oxidation and drainage of organic soils were identified as primary drivers of subsidence.
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Affiliation(s)
| | - Sahand Vahabzadeh
- Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran
| | - Dragan Pamucar
- University of Belgrade, Faculty of Organizational Sciences, Department of Operations Research and Statistics, Jove Ilića 154, 11000, Belgrade, Serbia
- College of Engineering, Yuan Ze University, Taoyuan City, 320315, Taiwan
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Nadiri AA, Moazamnia M, Sadeghfam S, Gnanachandrasamy G, Venkatramanan S. Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 304:119208. [PMID: 35351597 DOI: 10.1016/j.envpol.2022.119208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 06/14/2023]
Abstract
Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based on Convolutional Neural Network (CNN) to decrease subjectivity. This formulation considers three definitions of intrinsic, specific, and total vulnerabilities. Accordingly, three CNN models are trained and tested to calculate IVI, SVI, and TVI, respectively referring to the intrinsic, specific, and total vulnerability indices. The formulation is applied in an unconfined aquifer northwest of Iran and delineates hotspots within the aquifer. The area under curve (AUC) values derived by the receiver operating curves evaluate the vulnerability indices versus nitrate concentrations. The AUC values for BDF, IVI, SVI, and TVI are 0.81, 0.91, 0.95, and 0.95, respectively. Therefore, CNNs significantly improve the results compared to BDF, but IVI, SVI, and TVI have approximately identical performances. However, the visual comparison between their results provides evidence that significant differences exist between the spatial patterns despite identical AUC values.
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Affiliation(s)
- Ata Allah Nadiri
- Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, 166616471, East Azerbaijan, Iran; Institute of Environment, University of Tabriz, Tabriz, 5166616471, East Azerbaijan, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, 5618985991, Ardabil, Iran; Medical Geology and Environmental Research Center, University of Tabriz, Iran.
| | - Marjan Moazamnia
- Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, 166616471, East Azerbaijan, Iran.
| | - Sina Sadeghfam
- Department of Civil Engineering, Faculty of Engineering, University of Maragheh, P.O. Box 55136-553, Maragheh, East Azerbaijan, Iran.
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