GIS-Based Multi-criteria Evaluation for Deciphering of Groundwater Potential

Ahmad, Imran; Dar, Mithas Ahmad; Andualem, Tesfa Gebrie; Halefom, Afera; Tolosa, Asirat Teshome

doi:10.1007/s12524-019-01078-3

Cited by 13 publications

(8 citation statements)

References 10 publications

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“…The criteria selected for the delineation of potential groundwater zones in this catchment were ranked and indexed. The integration of all the key factors of the groundwater occurrence and movement is computed as GPI (Ahmad, Dar, Teka, et al, 2020). GPI is a unitless parameter used to index (equation (5)) the probability of occurrence of groundwater potential zones in a given catchment.…”

Section: Methodsmentioning

confidence: 99%

An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

Tamiru

Wagari

Tadese

2022

Air, Soil and Water Research

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In this paper, the performance of Artificial Intelligence (AI) in Geospatial analysis and GIS platforms for the prospecting of potential groundwater zones was evaluated in Fincha catchment, Abay, Ethiopia. Components of geospatial data under morphometric, hydrologic, permeability, and surface dynamic change were confirmed as the criteria for prospecting groundwater potential zones. The influence of the individual criterion was ranked and weighted in Artificial Neural Networks (ANN) training model and Analytical Hierarchy Process (AHP). The correctness of the weights fixed in the ANN and AHP was evaluated with target data assigned to the networks and consistency index (CI) respectively. The weighted overlay analysis in the GIS environment was implemented to generate the promising zones in both approaches (ANN and GIS). The results obtained in the ANN model and GIS were evaluated based on pumping rate and ground-truthing points. Groundwater potential zones of five and four classes were delineated in AI and GIS techniques respectively, and this is an indicator for the effectiveness of AI in geospatial analysis for prospecting of potential zones than the traditional GIS technique. The percentage of accuracy in both methods was measured from the ROC curve and AUC. Therefore, it was found that the delineated groundwater potential zones and the ground-truthing points were agreed with 96% and 91% in the AI and GIS platforms respectively. Finally, it is concluded that the ANN model is an effective tool for the delineation of groundwater prospective zones.

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Section: Methodsmentioning

confidence: 99%

An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

Tamiru

Wagari

Tadese

2022

Air, Soil and Water Research

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“…Andualem and Demeke (2019) used the same methodology for mapping the groundwater potential of the Guna Tana landscape, Ethiopia, and found that about 23.8% and 13.4% of the area had excellent and very good groundwater prospects respectively. Ahmad et al (2019a) tried to decipher the groundwater potential of the Wonbera Basin, Ethiopia using GIS-Based Multi-criteria Evaluation and stated that about 48.01% of the area revealed good groundwater potential. Besides, Ahmad et al (2019b) also studied the groundwater potential of the Beles Sub-basin, Ethiopia using GIS and found that 19.34% of the area had high groundwater potential.…”

Section: Identification Of Groundwater Potential Zonementioning

confidence: 99%

Geospatial and multi-criteria decision approach of groundwater potential zone identification in Cuma sub-basin, Southern Ethiopia

Hagos

Andualem

2021

Heliyon

Self Cite

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Groundwater is an essential component of our country's freshwater supplies. It plays a critical role in satisfying the water demands of the nation's many user sectors. The groundwater resource cannot be exploited and sustained optimally unless prospective zones are identified prior to the drilling of wells. The current study intended to analyze groundwater potential possibilities in the Cuma sub-basin, Omo Gibe basin, southern Ethiopia, utilizing geospatial and multi-criteria decision analysis approaches. For this purpose, a range of thematic layers like geology, Rainfall, drainage density, slope, land use and land cover (LULC), Soil type, faults density, saturated hydraulic conductivity, Available water capacity, and soil depth were organized for the study area. The different sub-criteria of each theme layer were rated according to their effect on groundwater recharge, and a weightage was assigned to each thematic layer based on the analytical hierarchy method (AHP). The identification of groundwater-potential regions of the sub-basin was one of the study's main results. The resulting groundwater potential zone map is further classified into five groundwater potential classes: very good (7.01%), good (19.49%), moderate (17.48%), poor (29.51%), and very poor (26.51%). The study's findings have important significance for developing sustainable groundwater strategies in the area.

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“…The heavy metal contamination has a significant role in human health hazardous. Heavy metals in groundwater are analyzed and their sources and impacts are identified using SMCE method and risk assessment (Imran Ahmad et al 2019;Vasant Madhav Wagh et al 2019). As shown in Table 8, Spatial Multi-Criteria Evaluation Rank of Risk Hazardous of Groundwater is generated.…”

Section: Spatial Multi-criteria Evaluation (Smce) Analysis For Risk Hazardous Of Groundwatermentioning

confidence: 99%

Assessment and source identification of heavy metal contamination of groundwater using geospatial technology in Gadilam River basin, Tamil Nadu, India

Venugopal¹,

Suresh²

2021

Appl Water Sci

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The purpose of this study is to determine the groundwater quality of the Gadilam River basin on the basis of Geographic Information Technology (GIS) taxonomy evaluation and risk assessment and statistical analysis. The Gadilam River originates from the district of Kallakurichi and flows into the district of Viluppuram finally confluence at Bay of Bengal in the district of Cuddalore. Groundwater supplies are serving the entire drinking needs of the residents of the Gadilam River basin, and pollution of its toxic elements has been raised in recent years. A total of 120 groundwater samples, excluding reserved forest areas, were obtained from the Gadilam River basin. 50 samples of the Archaean formation, 34 samples of the Quaternary formation, 35 additional Tertiary formation samples and one Cretaceous formation sample. During the monsoon season (Nov. 2018), groundwater samples were taken, which is after the Gaja cyclone’s diastral effects. At the time of the sample acquisition, pH, EC and TDS were measured using the Elico field kit. Using the Atomic Absorption Spectrometer, the heavy metals Zn, Cr, Cu, Pb, Co, Fe, Mn and Ni were analyzed. The spatial distribution maps of the GIS shows that in all the samples the concentrations of chromium, lead, cobalt, nickel are high (above the permitted limit). In 93.40%of the area, the manganese concentrations are higher, dropping under the unauthorized cap for human use in compliance with WHO requirements. In addition, statistical analysis indicates that groundwater pollution of the roots of Pb and Cr is from foreign sources and geogenic origin cannot be avoided. The country's rocks and industrial effluents can trigger Zn and Co. The anthropogenic operations may be due to Cu. The taxonomy diagram showing all groundwater samples is classified as ‘near neutral high metal.’ Industrial waste, household sewage, has boosted the emission of heavy metals. Groundwater risk assessment study of heavy metals using SMCE approach reveals that dense residential area in the Archaean formation, lineaments intersection with river portion and sea shore area in Quaternary formation and nearby SIPCOT industry and Lignite mining area in Tertiary formation is noted in the ‘Extreme risk dangerous’ areas.

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GIS-Based Multi-criteria Evaluation for Deciphering of Groundwater Potential

Cited by 13 publications

References 10 publications

An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

Geospatial and multi-criteria decision approach of groundwater potential zone identification in Cuma sub-basin, Southern Ethiopia

Assessment and source identification of heavy metal contamination of groundwater using geospatial technology in Gadilam River basin, Tamil Nadu, India

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