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

Nadiri, Ata Allah; Moazamnia, Marjan; Sadeghfam, Sina; Barzegar, Rahim

doi:10.3390/w13192622

Cited by 11 publications

(2 citation statements)

References 38 publications

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“…An MFL model can be described as follows: (a) output membership functions have fuzzy properties when using the "min" operation; (b) rules are determined through clustering by the FCM method [37]. The fuzzy implication operations in LFL are similar to those in MFL, but the product operator is used instead of the permutation operator [38]. Since water quality parameters are uncertain, calculating the nonlinear output may be more effective using a membership function [39].…”

Section: Mamdani Fuzzy Logic (Mfl)mentioning

confidence: 99%

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Nadiri

Barzegar

Sadeghfam

et al. 2022

Water

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The study of groundwater quality is typically conducted using water quality indices such as the Groundwater Quality Index (GQI) or the GroundWater Quality Index (GWQI). The indices are calculated using field data and a scoring system that uses ratios of the constituents to the prescribed standards and weights based on each constituent’s relative importance. The results obtained by this procedure suffer from inherent subjectivity, and consequently may have some conflicts between different water quality indices. An innovative feature drives this research to mitigate the conflicts in the results of GQI and GWQI by using the predictive power of artificial intelligence (AI) models and the integration of multiple water quality indicators into one representative index using the concept of data fusion through the catastrophe theory. This study employed a two-level AI modeling strategy. In Level 1, three indices were calculated: GQI, GWQI, and a data-fusion index based on four pollutants including manganese (Mn), arsenic (As), lead (Pb), and iron (Fe). Further data fusion was applied at Level 2 using supervised learning methods, including Mamdani fuzzy logic (MFL), support vector machine (SVM), artificial neural network (ANN), and random forest (RF), with calculated GQI and GWQI indices at Level 1 as inputs, and data-fused indices target values derived from Level 1 fusion as targets. We applied these methods to the Gulfepe-Zarinabad subbasin in northwest Iran. The results show that all AI models performed reasonably well, and the difference between models was negligible based on the root mean square errors (RMSE), and the coefficient of determination (r2) metrics. RF (r2 = 0.995 and RMSE = 0.006 in the test phase) and MFL (r = 0.921 and RMSE = 0.022 in the test phase) had the best and worst performances, respectively. The results indicate that AI models mitigate the conflicts between GQI and GWQI results. The method presented in this study can also be applied to modeling other aquifers.

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Section: Mamdani Fuzzy Logic (Mfl)mentioning

confidence: 99%

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Nadiri

Barzegar

Sadeghfam

et al. 2022

Water

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“…Case studies involving surface subsidence include [19][20][21][22][23]. Recent attempts to analyze models and predict subsidence using artificial intelligence methods in real case scenarios combined also with decision making and risk management methodologies can be found in [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A New Analytical Method for Calculating Subsidence Resulting by Fluid Withdrawal from Disk-Shaped Confined Aquifers

Gravanis,

Sarris

2023

Water

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This work presents the derivation of analytical solutions concerning the radial subsidence distribution ensuing from fluid extraction from a disk-shaped confined aquifer in homogeneous formations. The study draws upon methodologies developed in petroleum geomechanics of deep reservoirs to estimate surface uplift due to CO2 injection using Hankel-transformed thin plate theory. These methods yield simplified expressions as compared to previous results derived using the superposition principle on surface uplift from a uniform pressure field. Hence, closed-form formulas for the subsidence at the well location are re-derived, while the formulas for the subsidence field are deducted by both methods and the mathematical relation between the two methodologies is discussed. Additionally, innovative closed-form asymptotic solutions for radial subsidence distribution are deduced for scenarios involving deep aquifers. These solutions demonstrate exceptional accuracy when aquifer depth exceeds aquifer diameter, exhibiting independence from formation permeability and fluid viscosity. The study explores the influence of physical parameters on the subsidence field.

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Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence

Wei,

Chen,

Zhao

et al. 2023

Nat Hazards

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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

Cited by 11 publications

References 38 publications

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

Developing a Data-Fused Water Quality Index Based on Artificial Intelligence Models to Mitigate Conflicts between GQI and GWQI

A New Analytical Method for Calculating Subsidence Resulting by Fluid Withdrawal from Disk-Shaped Confined Aquifers

Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence

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