Artificial intelligence modelling framework for mapping groundwater vulnerability of fractured aquifer

Msaddek, Mohamed Haythem; Moumni, Yahya; Ayari, Ahmed; May, Moufida El; Chenini, Ismail

doi:10.1080/10106049.2022.2037729

Cited by 11 publications

(11 citation statements)

References 56 publications

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“…Notably, the recent Quaternary deposits within the study region consist of permeable formations. The variation in the depth of the Mio-Plio-Quaternary sand and gravel accumulations primarily stems from irregularities in bedrock topography [27,[37][38][39][40][41][42]. As one moves eastward within the study area, the depth of the phreatic aquifer increases [27,37], but its spatial extent remains constrained due to the surrounding elevations encompassing the hydrogeological basin.…”

Section: Study Areamentioning

confidence: 97%

“…The variation in the depth of the Mio-Plio-Quaternary sand and gravel accumulations primarily stems from irregularities in bedrock topography [27,[37][38][39][40][41][42]. As one moves eastward within the study area, the depth of the phreatic aquifer increases [27,37], but its spatial extent remains constrained due to the surrounding elevations encompassing the hydrogeological basin. On average, the depth of this formation measures around 65 m. The shallow aquifer's porosity is estimated at 28% [37,38].…”

Section: Study Areamentioning

confidence: 97%

“…Its location lies within the southern central Atlas of Tunisia, known for extensive mining activities, especially phosphate and gypsum extraction [37]. This basin constitutes an independent hydrogeological unit [27,37] and is underpinned by a diverse array of geological formations. The characterization of the Meknassy basin's geology (Figure 1) is compiled by studying published geological maps and conducting firsthand observations on-site.…”

Section: Study Areamentioning

confidence: 99%

“…Effectively addressing this concern is crucial for ensuring robust environmental stewardship, particularly by anticipating variations in water quality indicators. Consequently, there has been a recent surge in the adoption of deterministic models within this domain [15][16][17]20,21,24,[26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Groundwater Quality Evaluation of Fractured Aquifers Using Machine Learning Models and Hydrogeochemical Approaches to Sustainable Water-Irrigation Security in Arid Climate (Central Tunisia)

Msaddek,

Moumni,

Zouhri

et al. 2023

Water

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The primary aims of this research paper involve the creation and verification of machine learning-based quality models that utilize Integrated Irrigation Water Quality Indices (IIGWQIs) through an integrated GIS approach. We utilize the Least-Squares Support Vector Machines (LS-SVM) and the Pearson Correlation Fuzzy Inference-based System (PC-FIS) to establish forecasts for groundwater quality in the Meknassy basin. This basin serves as a representative case of an irrigated region in a mining environment under arid climatic conditions in central Tunisia. The evaluated factors for groundwater quality encompass the Irrigation Water Quality Index (IWQIndex), Sodium Adsorption Ratio Index (SARIndex), Soluble Sodium Percentage Index (SSPIndex), Potential Salinity Index (PSIndex), Kelley Index (KIndex), and Residual Sodium Carbonate Index (RSCIndex). These factors were determined based on measurements from 53 groundwater wells, which included various physico-chemical parameters. The hydrogeochemical facies identified included Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, and Na-Cl facies, revealing processes such as carbonate weathering, carbonate dissolution, interactions between rocks and groundwater, and mixing ionic substitution. In terms of the irrigation suitability categories, the IWQIndex, SSPIndex, PSIndex, Kindex, and RSCIndex indicated no limitation or minimal limitation (77.36%), secure (92.45%), favorable to excellent (66.04%), favorable (81.13%), and average to secure (88.68%), respectively. However, only 15.09% were considered favorable, according to SARIndex. The evaluation of the predictive models revealed the effectiveness of both the PC-FIS model and the LS-SVM model in accurately forecasting the IIGWQIs.

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Section: Study Areamentioning

confidence: 97%

Section: Study Areamentioning

confidence: 97%

Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Groundwater Quality Evaluation of Fractured Aquifers Using Machine Learning Models and Hydrogeochemical Approaches to Sustainable Water-Irrigation Security in Arid Climate (Central Tunisia)

Msaddek,

Moumni,

Zouhri

et al. 2023

Water

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“…To improve the assessment of groundwater contamination vulnerability advanced multiple machine learning (ML) models (such as Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR)) were utilized (Elzain et al, 2022). In another work the conventional DRASTIC model is modi ed by adding a new parameter of fracture media to map the fractured aquifer vulnerability (Msaddek et al, 2022). Furthermore, there are other works that study impact of various contaminants on the groundwater.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the DRASTIC + L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods

2023

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One of the most important and perilous groundwater contaminants is nitrate, which is considered a potential environmental risk in Iran and an international issue globally. The study area is the Karaj uncon ned aquifer, Central Iran, subjected to quantity and quality hazards due to unbalanced urban, industrial, and agricultural developments. Evaluation of the vulnerability condition of this aquifer against nitrate as the Contamination index is essential since the most amount of consumption water in the area is provided from groundwater. The study aims to detect vulnerable zones of the Karaj aquifer using DRASTIC + L and Modi ed VEBHAT vulnerability models. The weighting of the parameters has been performed employing the recent Multiple-Criteria Decision-Making (MCDM) methods, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Best Worst Method (BWM). The results demonstrated that the most vulnerability had occurred from the east to the south of the aquifer (high to super-critical), including urban and residential areas, regarding both the vulnerability models' plots. The plots of the two methods are similar and did not exhibit considerable differences. The Modi ed VEBHAT has more certainty than the DRASTIC + L and has optimized and improved the DRASTIC + L model because the most vulnerable zone in the output plots of the Modi ed VEBHAT model is in the east of the aquifer, which has good agreement with the nitrate contaminant mass with the highest concentration.

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Harnessing machine learning tools for water quality assessment in the Kebili shallow aquifers, Southwestern Tunisia

Kraiem,

Zouari,

Trabelsi

2024

Acta Geochim

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Artificial intelligence modelling framework for mapping groundwater vulnerability of fractured aquifer

Cited by 11 publications

References 56 publications

Groundwater Quality Evaluation of Fractured Aquifers Using Machine Learning Models and Hydrogeochemical Approaches to Sustainable Water-Irrigation Security in Arid Climate (Central Tunisia)

Groundwater Quality Evaluation of Fractured Aquifers Using Machine Learning Models and Hydrogeochemical Approaches to Sustainable Water-Irrigation Security in Arid Climate (Central Tunisia)

Comparison of the DRASTIC + L and modified VABHAT models in vulnerability assessment of Karaj aquifer, central Iran, using MCDM, SWARA, and BWM methods

Harnessing machine learning tools for water quality assessment in the Kebili shallow aquifers, Southwestern Tunisia

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