Mapping potential groundwater accumulation zones for Karachi city using GIS and AHP techniques

Ahmad, Ibtihaj; Hasan, Hamna; Jilani, Moeid Mujeeb; Ahmed, S. I.

doi:10.1007/s10661-023-10971-x

Cited by 13 publications

(3 citation statements)

References 54 publications

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“…Continuously increasing groundwater consumption due to massive population density, a growing area of irrigated farming and less environmentally significant economic advancement has put enormous stress on its prudent utilization (Lecart et al 2024;Elvis et al 2023;Sherif et al 2023;Alsafadi et al 2023). Globally, domestic, industrial and agricultural applications account for approximately 36, 27, and 42% of total groundwater use, respectively (Taylor Extended author information available on the last page of the article et al 2013;Bera et al 2020;Genjula et al 2023;Ahmad et al 2023).…”

Section: Introductionmentioning

confidence: 99%

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

Abdo,

Vishwakarma,

Alsafadi

et al. 2024

Appl Water Sci

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In light of population growth and climate change, groundwater is one of the most important water resources globally. Groundwater is crucial for sustaining many vital sectors in Syria, including industrial and agricultural sectors. However, groundwater exploitation has significantly escalated to meet different water needs especially in the post-war period and the earthquake disaster. Therefore, the goal was this study delineation of the groundwater potential zones (GPZs) by integrating the analytic hierarchy process (AHP) method in a geographic information systems (GIS) within the AlAlqerdaha river basin in western Syria. In this study, ten criteria were used to map the spatial distribution of GPZs, including slope, geomorphology, drainage density, land use/land cover (LU/LC), lineament density, lithology, rainfall, soil, curvature and topographic wetness index (TWI). GPZs map was validated by using the location of 74 wells and the Receiver Operating Characteristic Curve (ROC). The findings suggest that the study area is divided into five GPZs: very low, 21.39 km2 (10.87%); low, 52.45 km2 (26.65%); moderate, 65.64 km2 (33.35%); high, 40.45 km2 (20.55%) and very high, 16.90 km2 (8.58%). High and very high zones mainly corresponded to the western regions of the study area. The conducted spatial modeling indicated that the AHP-based GPZs map showed a remarkably acceptable correlation with wells locations (AUC = 87.7%, n = 74), demonstrating the precision of the AHP–GIS as a rating method. The results of this study provide objective and constructive outputs that can help decision-makers to optimally manage groundwater resources in the post-war phase in Syria.

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Section: Introductionmentioning

confidence: 99%

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

Abdo,

Vishwakarma,

Alsafadi

et al. 2024

Appl Water Sci

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“…GIS is one of these methods (Geographic Information Systems). The administration of spatial data is done using a GIS, which is a kind of computer system (Ahmad et al, 2023). Additionally, the GIS is a vital and practical tool for managing water resources, estimating water availability, assessing water quality indicators, and ensuring the health of the aquifer can be sustained (Perera et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Geochemical Characterization and Water Quality Index Based Assessment of Groundwater Quality Using GIS In Tropical River Basin: Case Study in Kirama-Ara River Basin, Southern Province, Sri Lanka.

Dissanayake

Perera

Jayasinghe

2023

Preprint

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In the Kirama-ara River basin, Matara, southern region of Sri Lanka, a variety of agricultural inputs are utilized, and flooding is a common hazard. Therefore, the groundwater in this area is under serious threat. But, the complete physicochemical profile of the groundwater in the Kirama-ara river basin is yet undiscovered. Therefore, the goals of this study were to (a) identify the geochemical characteristics and changes in water quality, (b) evaluate non-carcinogenic health risk and develop a Groundwater quality index (GWQI), and (c) generate a spatial distribution map for groundwater physicochemical characters in the Kirama-ara river basin in Matara, Sri Lanka. The physiochemical characteristics of 16 (G1 to G16) groundwater samples, including pH, total dissolved solids (TDS), electrical conductivity (EC), Ca2+, Mg2+, Na+, K+, Cl−, NO3−, PO43−, and SO42−, were examined for spatial and chronological variations. The pH, NO3−, and PO43− values of 50% (G1,G4,G7,G12,G13,G14,G15,G16), 6.25% (G16), and 6.25% (G16) of the samples, respectively, were determined to be exceeded the allowable limit by both the WHO and SLS standards. The physicochemical investigation demonstrated that while 87.5% of the locations were acceptable for drinking, 12.5% of them lacked sufficient amounts. The most common hydro-chemical facies were Ca2+-Mg2+-HCO3−, mixed Ca2+-Mg2+-Cl− face, and Ca2+-Cl−. Rock weathering has a weighty effect on the ionic composition and chemistry of groundwater in the study area. Additionally, health risk assessment revealed that children face greater health risks than adults. Further investigations should be conducted to build reasonable management plans for long-term groundwater quality conservation and public health.

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“…Additionally, researchers have proposed diverse methodologies to enhance the efficiency and the accuracy of prediction models, including ensemble models and optimization models. Besides, despite the development and the efficiency of artificial intelligence algorithms (AIAs), many researchers prefer to use multi-criteria decision-making as analytical hierarchy process heuristic model (Ahmad et al, 2023;Shelar et al, 2023). Indeed, GWPM validation can be supported with other additional data such as electrical resistivity tomography (Sangawi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

Jari,

Bachaoui,

Hajaj

et al. 2023

Front. Water

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Groundwater resource management in arid regions has a critical importance for sustaining human activities and ecological systems. Accurate mapping of groundwater potential plays a vital role in effective water resource planning. This study investigates the effectiveness of machine learning models, including Random Forest (RF), Adaboost, K-Nearest Neighbors (KNN), and Gaussian Process in groundwater potential mapping (GWPM) in the Tan-Tan arid region, Morocco. Fourteen groundwater conditional factors were considered following multicollinearity test, including topographical, hydrological, climatic, and geological factors. Additionally, point data with 174 sites indicative of groundwater occurrences were incorporated. The groundwater inventory data underwent random partitioning into training and testing datasets at three different ratios: 55/45%, 65/35%, and 75/25%. Ultimately, a comprehensive ranking of the 13 models, encompassing both individual and ensemble models, was determined using the prioritization rank technique. The results revealed that ensemble learning (EL) models, particularly RF and Adaboost (RF-Adaboost), outperformed individual models in groundwater potential mapping. Based on accuracy assessment using the validation dataset, the RF-Adaboost EL results yielded an Area Under the Receiver Operating characteristic Curve (AUROC) and Overall Accuracy (OA) of 94.02 and 94%, respectively. Ensemble models have been effectively applied to integrate 14 factors, capturing their intricate interrelationships, and thereby enhancing the accuracy and robustness of groundwater prediction in the Tan-Tan water-scarce region. Among the natural factors, the current study identified lithology, structural elements (such as faults and tectonic lineaments), and land use as significant contributors to groundwater potential. However, the critical characteristics of the study area showing a coastal position as well as a low background in groundwater prospectivity (low borehole points) are challenging in GWPM. The findings highlight the importance of the significant factors in assessing and managing groundwater resources in arid regions. Moreover, this study makes a contribution to the management of groundwater resources by demonstrating the effectiveness of ensemble learning algorithms in the groundwater potential mapping (GWPM) in arid regions.

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Mapping potential groundwater accumulation zones for Karachi city using GIS and AHP techniques

Cited by 13 publications

References 54 publications

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

GIS-based multi-criteria decision making for delineation of potential groundwater recharge zones for sustainable resource management in the Eastern Mediterranean: a case study

Geochemical Characterization and Water Quality Index Based Assessment of Groundwater Quality Using GIS In Tropical River Basin: Case Study in Kirama-Ara River Basin, Southern Province, Sri Lanka.

Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco

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