A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability

Agoubi, Belgaçem; Radhia, Dabbaghi; Kharroubi, Adel

doi:10.1111/gwat.12634

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…While groundwater exploration and production are crucial, the current social demand emphasizes the need for groundwater resource vulnerability/protection. Vulnerability appraisal is a comprehensive and cardinal step in examining groundwater filth (Agoubi et al 2018;Rizka 2018;George 2021a). The applicability of groundwater is most often defiled by leakage of leachate plumes from landfills, oil adulteration and dissipation water (from run-off/flood, toilets, oil-ceiling pipelines, and infected vessels) (Makeig 1982).…”

Section: Introductionmentioning

confidence: 99%

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Eze,

Essien,

Edirin

et al. 2023

BJESR

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Exploration, management, and conservation of groundwater resources are critical stages toward potable water supply, driven by an expanding populace and the threat of a new norm posed by the distinctive coronavirus (COVID-19) pandemic. An in-depth assessment of the potential of groundwater reserves and susceptibility, using a multi-criteria evaluation, is required to aid in the planning of exploration programs for groundwater well location. Thirty (30) vertical electrical soundings (VES) were collected in Okerenkoko, Warri-Southwest, Delta State, to assess groundwater potential and vulnerability indicators. The VES data were used to obtain the first-order geoelectric variables, which were further exploited to calculate the geo-hydraulic parameters (hydraulic conductivity and transmissivity) and the vulnerability indices of the aquifer. For aquifer vulnerability appraisal, the AVI (aquifer vulnerability index), GOD (groundwater occurrence, overlying lithology, and depth to the aquifer), and GLSI (geoelectric layer susceptibility index) models were used. The groundwater characteristics in the area were evaluated using the aquifer resistivity, thickness, transmissivity and coefficient of anisotropy values of the aquifer layers defined from VES 1-30. The results show that aquifer layers with low resistivity favor more saturation due to immense porosity and therefore have greater groundwater potential than aquifers with high resistivity. The geoelectric structures defined by VES 1, 2 and 4 were consistent in their groundwater potential and yield judging from the multi-criteria assessments. The estimation of AVI, GOD, and GLSI models for aquifer threat assessment was facilitated by the multi-criteria evaluation of vulnerability indices utilizing hydro-geophysical parameters and index-based approaches. The models depend on the symbiotic effects of geologic array and thickness as the basis for the magnitude of conservation imparted to any particular aquifer involved. The AVI model map depicts that most of the VES locations were rated high (C between 1 and 2) to extremely high (C < 1), indicating that the aquifers at these locations are vulnerable to pollution. However, the extent of vulnerability observed in the GOD model is less than in the AVI model, as GOD accords much more inclination to the inherent properties of geologic entities. The GOD model map categorized the vulnerability index ratings in the area as negligible (0.0-0.1), low (0.1-0.3) and moderate (0.3-0.5), with most VES locations ranked low to moderate, which indicates that these locations are susceptible to vulnerability. In the GLSI model, individual overlying layer thicknesses were prioritized. The GLSI model map shows that the vulnerability index ratings in the area are ranked as moderate (2.00-2.99), high (3.00-3.99) and extremely high (≥ 4.00) with most of the VES locations ranked moderate to high with the exception of VES 27, which ranked extremely high in both AVI and GLSI indices. By correlating the results of vulnerability index valuation for the AVI, GOD and GLSI models, more correlation was observed between the AVI and GLSI models. These findings validate the adoption of a multi-criteria evaluation methodology for groundwater potential and aquifer vulnerability studies and are strongly recommended as practical criteria for locating subsurface aquifers and their protective measures for groundwater prospect development planning and management.

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Section: Introductionmentioning

confidence: 99%

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Eze,

Essien,

Edirin

et al. 2023

BJESR

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“…While GW pumping may be regulated to minimize the likelihood of SWI, delineating and quantifying the factors that may trigger such intrusion is a crucial step in the process. Moreover, climate change is projected to result in rising sea levels and exacerbate the vulnerability of coastal aquifers to SWI [8][9][10][11][12]. A GW vulnerability assessment can be an effective step towards protecting GW resources [4,9], land use planning, and sustainable use of the GW resource [13].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, climate change is projected to result in rising sea levels and exacerbate the vulnerability of coastal aquifers to SWI [8][9][10][11][12]. A GW vulnerability assessment can be an effective step towards protecting GW resources [4,9], land use planning, and sustainable use of the GW resource [13].…”

Section: Introductionmentioning

confidence: 99%

Vulnerability of a Tunisian Coastal Aquifer to Seawater Intrusion: Insights from the GALDIT Model

Zghibi

Merzougui

Mansaray

et al. 2022

Water

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The Korba region in northwestern Tunisia has a coastal aquifer that is impacted by intensive irrigation, urban expansion, and sensitivity to SWI. We assessed the vulnerability extent of Korba’s GW to SWI. We utilized a parametric model for GW vulnerability assessment, the GALDIT, which considers six parameters to determine SWI effects. The GALDIT map has four rating categories (≥7.5, 7.5–5, 5–2.5, and <2.5), representing very high, high, moderate, and low vulnerability, respectively. Most of the region was found to be highly vulnerable (44.2% of the surface area), followed by areas characterized by very high (20.3%) and moderate (19.3%) vulnerability. Only 16.2% was found to have low vulnerability. A parameter sensitivity analysis showed that distance from shore and depth of GW represent the determining factors for SWI with variation index values of 24.12 and 18.02%, respectively. Inland advancement of seawater is causing GW salinity to rise, as indicated by a strong Pearson correlation coefficient of 0.75 between SWI indices and the electrical conductivity. Suitable areas for artificial recharge were mainly distributed in the alluvial plains, with a total area of 32.85 km2. Inhibiting SWI requires about 11.31 MCM of artificial recharge in the two most suitable recharge zones in the region.

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“…Intrinsic vulnerability connotes an aquifer that is susceptible to pollutions and as well as lithological layering and hydrogeological features. Vulnerability assessment is holistically an essential stride in evaluating groundwater contaminations (Agoubi et al 2018;Rizka, 2018;George, 2021a). This challenge calls for concerns and the need to scientifically delineate the frequently and economically exploitable hydrogeological units, mostly those that are liable to susceptibility and vulnerability from surface infiltrations in the habitable areas (Vu et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Integrating hydrogeological and second-order geo-electric indices in groundwater vulnerability mapping: A case study of alluvial environments

George

2021

Appl Water Sci

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AVI (Aquifer vulnerability index), GOD (groundwater occurrence, overlying lithology and depth to the aquifer), GLSI (geo-electric layer susceptibility indexing) and S (longitudinal unit conductance) models were used to assess economically exploitable groundwater resource in the coastal environment of Akwa Ibom State, southern Nigeria. The models were employed in order to delineate groundwater into its category of vulnerability to contamination sources using the first- and second-order geo-electric indices as well as hydrogeological inputs. Vertical electrical sounding technique employing Schlumberger electrode configuration was carried out in 16 locations, close to logged boreholes with known aquifer core samples. Primary or first-order geo-electric indices (resistivity, thickness and depth) measured were used to determine S. The estimated aquifer hydraulic conductivity, K, calculated from grain size diameter and water resistivity values were used to calculate hydraulic resistance (C) used to estimate AVI. With the indices assigned to geo-electric parameters on the basis of their influences, GOD and FSLI were calculated using appropriate equations. The geologic sequence in the study area consists of geo-electric layers ranging from motley topsoil, argillites (clayey to fine sands) and arenites (medium to gravelly sands). Geo-electric parametric indices of aquifer overlying layers across the survey area were utilized to weigh the vulnerability of the underlying water-bearing resource to the contaminations from surface and near-surface, using vulnerability maps created. Geo-electrically derived model maps reflecting AVI, BOD, FLSI and S were compared to assess their conformity to the degree of predictability of groundwater vulnerability. The AVI model map shows range of values of log C ( −3.46—0.07) generally less than unity and hence indicating high vulnerability. GOD model tomographic map displays a range of 0.1–0.3, indicating that the aquifer with depth range of 20.5 to 113.1 m or mean depth of 72. 3 m is lowly susceptible to surface and near-surface impurities. Again, the FLSI map displays a range of FLSI index of 1.25 to 2.75, alluding that the aquifer underlying the protective layer has a low to moderate vulnerability. The S model has values ranging from 0.013 to 0.991S. As the map indicates, a fractional portion of the aquifer at the western (Ikot Abasi) part of the study area has moderate to good protection (moderate vulnerability) while weak to poor aquifer protection (high vulnerability) has poor protection. The S model in this analysis seems to overstate the degree of susceptibility to contamination than the AVI, GOD and GLSI models. From the models, the categorization of severity of aquifer vulnerability to contaminations is relatively location-dependent and can be assessed through the model tomographic maps generated.

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A Mamdani Adaptive Neural Fuzzy Inference System for Improvement of Groundwater Vulnerability

Cited by 15 publications

References 16 publications

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Multi-Criteria Evaluation (MCE) of Groundwater Prospect and Vulnerability Index Mapping from Second-Order Geo-Electric Indices: A Case Study of Coastal Environments

Vulnerability of a Tunisian Coastal Aquifer to Seawater Intrusion: Insights from the GALDIT Model

Integrating hydrogeological and second-order geo-electric indices in groundwater vulnerability mapping: A case study of alluvial environments

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