An investigation into time-variant subsidence potentials using inclusive multiple modelling strategies

Gharekhani, Maryam; Nadiri, Ata Allah; Khatibi, Rahman; Sadeghfam, Sina

doi:10.1016/j.jenvman.2021.112949

Cited by 18 publications

(9 citation statements)

References 43 publications

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“…Three cases may be related to droughts impacting resilience, as follows. Depleting aquifers give rise to land subsidence, and its varying degrees have already been reported in relation to some of the aquifers surrounding the lake, see 12 – 14 . Subsidence undermines inherent land resilience and thereby undermine soil structures, agriculture, infrastructure, transport systems and buildings to the extent that future recoveries can be difficult if not impossible.…”

Section: Discussion Of the Findings And Their Implicationsmentioning

confidence: 86%

“…A saltpan of approximately 1 m deep is left behind the shrunken lake, the health impacts of which are evident but not yet documented. However, the depleting aquifers are already showing land subsidence problems at some of these aquifers, see 12 – 14 . Khatibi et al 23 take an overview of the past initiatives on restoring Lake Urmia and note that any material solution is yet to emerge from all those apparent project proposals.…”

Section: Resultsmentioning

confidence: 99%

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Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

Sadeghfam

Mirahmadi

Khatibi³

et al. 2022

Sci Rep

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A critical understanding of the water crisis of Lake Urmia is the driver in this paper for a basin-wide investigation of its Meteorological (Met) droughts and Groundwater (GW) droughts. The challenge is to formulate a data-driven modelling strategy capable of discerning anthropogenic impacts and resilience patterns through using 21-years of monthly data records. The strategy includes: (i) transforming recorded timeseries into Met/GW indices; (ii) extracting their drought duration and severity; and (iii) deriving return periods of the maximum drought event through the copula method. The novelty of our strategy emerges from deriving return periods for Met and GW droughts and discerning anthropogenic impacts on GW droughts. The results comprise return periods for Met/GW droughts and their basin-wide spatial distributions, which are delineated into four zones. The information content of the results is statistically significant; and our interpretations hint at the basin resilience is already undermined, as evidenced by (i) subsidence problems and (ii) altering aquifers' interconnectivity with watercourses. These underpin the need for a planning system yet to emerge for mitigating impacts and rectifying their undue damages. The results discern that aquifer depletions stem from mismanagement but not from Met droughts. Already, migration from the basin area is detectable.

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Section: Discussion Of the Findings And Their Implicationsmentioning

confidence: 86%

Section: Resultsmentioning

confidence: 99%

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

Sadeghfam

Mirahmadi

Khatibi³

et al. 2022

Sci Rep

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“…The paper utilized a two-level multiple modeling strategy, referred to as inclusive multiple modeling (IMM), and the previous studies (e.g., [11]) indicated that IMM could significantly improve the modeling performance. As discussed earlier, the paper selected FCS at Level 1 and SVM at Level 2.…”

Section: Discussionmentioning

confidence: 99%

“…The second approach employs the AI technique and formulates a prediction model to determine a relationship between the criteria affecting the issue (known as input data) and the measured data (known as target data). Several studies exist in the literature, which investigates different issues by the AI technique comprising groundwater vulnerability to pollution by support vector machine (SVM), cubist, random forest, and Bayesian artificial neural network [7], groundwater potential indexing by using an advanced ensemble machine learning model that integrates artificial neural networks (ANN) with RealAdaBoost (RAB) ensemble technique [8], groundwater risk to saltwater intrusion by ANN, Sugeno fuzzy logic (SFL), neuro-fuzzy (NF) and SVM [9], and LS problem by ANN and genetic algorithm (GA) [10,11]. The input dataset of the LS problem consists of factors that affect LS, and the output dataset consists of subsidence values obtained by remote sensing or field measurement.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the results by AI generally have higher performance than MCDM. The second group treats the vulnerability to LS as a time-variant problem and predicts the LS index in the near future by AI models [11]. The third group transfers vulnerability indexing to risk indexing by dividing the data layers into active and passive data layers [6,10].…”

Section: Introductionmentioning

confidence: 99%

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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

Nadiri

Moazamnia

Sadeghfam

et al. 2021

Water

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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.

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Land subsidence susceptibility mapping using PWRSTFAL framework and analytic hierarchy process: fuzzy method (case study: Damaneh-Daran Plain in the west of Isfahan Province, Iran)

Chitsazan

Rahmani

Ghafoury

2022

Environ Monit Assess

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An investigation into time-variant subsidence potentials using inclusive multiple modelling strategies

Cited by 18 publications

References 43 publications

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

Investigating meteorological/groundwater droughts by copula to study anthropogenic impacts

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

Land subsidence susceptibility mapping using PWRSTFAL framework and analytic hierarchy process: fuzzy method (case study: Damaneh-Daran Plain in the west of Isfahan Province, Iran)

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