Evaluation of groundwater vulnerability to pollution using a GIS-based multi-criteria decision analysis

Aydi, Abdelwaheb

doi:10.1016/j.gsd.2018.06.003

Cited by 45 publications

(28 citation statements)

References 31 publications

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“…The features represent the nine thematic layers including, the type of soil, land cover utilization (derivative of the existing RS data), slope (landscape), lithology, precipitation, geological construction, geomorphology, lineament concentration and drainage compactness (Mahmoud and Alazba, 2016;Alharbi and Zaidi, 2018;Gubran et al, 2019). The various approaches were used and executed in generating groundwater potential area mapping, also an appropriateness model was created using the model builder of ArcGIS (Aydi, 2018). This suitability model creates an appropriate map for groundwater potential zones to generate different thematic maps such as the type of soil, land utilization and land cover, lithology, slope, precipitation, geological construction, geomorphology, lineament concentration and drainage compactness while utilizing the Weighted Overlay Process (WOP), as well as using both raster and vector databases.…”

Section: Central Region (Al-riyadh Province)mentioning

confidence: 99%

Water Resource Evaluation and Identifying Groundwater Potential Zones in Arid area using Remote Sensing and Geographic Information System

Kamruzzaman

Alanazi

Alruwaili

et al. 2020

Journal of Computer Science

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Section: Central Region (Al-riyadh Province)mentioning

confidence: 99%

Water Resource Evaluation and Identifying Groundwater Potential Zones in Arid area using Remote Sensing and Geographic Information System

Kamruzzaman

Alanazi

Alruwaili

et al. 2020

Journal of Computer Science

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“…OECD [39] reports several methods to normalize the factors: z-score, distance to a reference, ranking, logarithmic transformation, percentage of mean and min-max. Other authors have used weighting methods such as fuzzy function [43], comparison to threshold value [44], AHP [45] and expert judgement [23].…”

Section: Normalization Of Factorsmentioning

confidence: 99%

“…Country Scale Alessa et al [28] Canada Community Anandhi et al [10] USA Regional Aydi et al [43] Tunisia Regional Baalousha [60] Qatar National Baki et al [61] Morocco River basin Banton and Villeneuve [62] USA Municipality Boruff et al [63] Australia Regional Bâr et al [64] Switzerland Catchment Cai et al [21] China National Chen et al [65] Taiwan River basin Connell and Daele [47] UK Watershed Gain et al [4] Bangladesh River basin Hamouda et al [44] Egypt River basin Haryanto [66] Indonesia Provincial Javadi et al [67] Iran River basin Jun et al [68] Korea River basin Junior et al [5] Portugal River basin Leone et al [69] Hungary Watershed Masetti et al [55] Italy Municipality Nobre et al [70] Brazil Municipality Plummer et al [22] Canada Community Ribeiro et al [71] Ecuador Regional Shabbir and Ahmad [45] Pakistan Cities Shen et al [23] China Metropolitan Srinivasan et al [72] India Municipality [1] South Africa Basin/Watershed Tesoriero and Voss [49] China River basin Wang et al [7] China Regional Wang et al [73] China Regional Wu et al [27] China River basin Xia et al [74] China River basin Yanhui et al [46] China River basin and institutional features.…”

Section: Referencementioning

confidence: 99%

“…The method used to normalize the selected indicators are various: the min-max method [68], fuzzy function [43], ranking by experts [10], comparison of the indicator value to threshold or a reference [46], standardized beta coefficients [63] and empirical method. The weighting method is generally based on expert opinion [1], participative approach with stakeholders [22], AHP [5], LR [4], Delphi technique [73] and empirical method [71].…”

Section: Referencementioning

confidence: 99%

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Analysis of Water Resources Vulnerability Assessment Tools

Soumaila¹,

Niandou²,

Naïmi³

et al. 2019

JAST-B

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Water resources face many pressures and stresses around the world. Assessing the vulnerability of water resources is, therefore, increasingly an essential practice in water resources management. Several studies of water resources vulnerability have been carried out around the world during the last three decades. Thus, the main objective of this study was to analyze water resources vulnerability assessment tools and identify the methodological trend of water resources vulnerability assessment at the local level in existing literature. To achieve this objective, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method has been applied to include or exclude articles. Articles on water vulnerability numbering 374 were preselected in the ScienceDirect and the Google Scholar databases. Only 32 out of the 374 articles met the inclusion criteria defined for the analysis. The results show that China (21.9%), U.S.A. (9.4%) and Canada (6.3%) are the most active countries publishing water resources vulnerability assessments. Some 191 vulnerability indicators from the 32 articles were classified into five categories of factors: physical factors (33.5%), socioeconomic factors (28.3%), environmental or eco-environmental factors (25.1%), institutions and governance factors (7.3%) and infrastructure factors (5.8%). This study shows that almost half of the studies (47%) considered all five categories of factors, 9.4% considered four categories of factors, 6.3% considered three categories of factors, 28% used two types of factors and 9.4% considered only one category. Researchers have used several methods to assess water resources vulnerability: overlay and index methods (59.4%), process-based models (12.5%), statistical methods (9.4%), overlay and index methods plus process-based model (9.4%), overlay and index methods plus statistical methods (6.3%) and statistical methods plus process-based models (3.1%). Geographic information system (GIS) is an important tool in assessing the vulnerability of water resources with almost 60% of the studies using it at some stage of the assessment. In recent years, there is an increasing trend toward conceptualizing the vulnerability of water resources in a holistic way.

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“…Unlike the function model, the index method constructs an evaluation index system mainly based on the connotation of water resources vulnerability, the influencing factors of water resources vulnerability, DRASTIC model, and Drive, Pressure, State, Impact and Reaction (DPSIR) concept model [ 17 , 18 , 19 , 20 ]. Then, the weights of the evaluation indexes are determined mainly by the means of an Analytic Hierarchy Process (AHP), principal component analysis, or entropy weight [ 21 , 22 , 23 ]. In addition to the aforementioned approaches, fractal theory, fuzzy matter-element evaluation model, the projection pursuit model, and set pair analysis are also used to evaluate the water resources vulnerability [ 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Water Resources Vulnerability by Using a Rough Set Cloud Model: A Case Study of the Huai River Basin, China

Chen

Feng

Zhang

2018

Entropy

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Assessing water resources vulnerability is the foundation of local water resources management. However, as one of the major water systems in China, there is no existing evaluation index system that can effectively assess water resource vulnerability for the Huai River basin. To address this issue, we identified key vulnerability factors, constructed an evaluation index system, and applied such system to evaluate water resources vulnerability for the Huai River basin empirically in this paper. Specifically, our evaluation index system consists of 18 indexes selected from three different aspects: water shortage, water pollution, and water-related natural disaster. Then, the improved blind deletion rough set method was used to reduce the size of the evaluation index while keep the evaluation power. In addition, the improved conditional information entropy rough set method was employed to calculate the weights of evaluation indexes. Based on the reduced index system and calculated weights, a rough set cloud model was applied to carry out the vulnerability evaluation. The empirical results show that the Huai River basin water resources were under severe vulnerability conditions for most of the time between 2000 and 2016, and the Most Stringent Water Resources Management System (MS-WRMS) established in 2012 did not work effectively as expected.

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Evaluation of groundwater vulnerability to pollution using a GIS-based multi-criteria decision analysis

Cited by 45 publications

References 31 publications

Water Resource Evaluation and Identifying Groundwater Potential Zones in Arid area using Remote Sensing and Geographic Information System

Water Resource Evaluation and Identifying Groundwater Potential Zones in Arid area using Remote Sensing and Geographic Information System

Analysis of Water Resources Vulnerability Assessment Tools

Assessing Water Resources Vulnerability by Using a Rough Set Cloud Model: A Case Study of the Huai River Basin, China

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