Groundwater contamination vulnerability assessment using DRASTIC method, GSA, and uncertainty analysis

Dizaji, Alireza Razavi; Hosseini, Seyed Abbas; Rezaverdinejad, Vahid; Sharafati, Ahmad

doi:10.1007/s12517-020-05650-x

Cited by 14 publications

(5 citation statements)

References 41 publications

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“…Other authors concur that these parameters are the most significant [3,70,71]. The principal change in O-DRASTIC with respect to the original DRASTIC is in the weights of parameters, which highlights that these parameters embrace most of the uncertainty in the DRASTIC vulnerability assessment [72].…”

Section: Discussionmentioning

confidence: 86%

A Novel Approach to Harmonize Vulnerability Assessment in Carbonate and Detrital Aquifers at Basin Scale

Baena-Ruíz

Pulido-Velázquez

2020

Water

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The DRASTIC (D: Depth to water; R: Net recharge; A: Aquifer media; S: Soil media; T: Topography; I: Impact of vadose zone; C: Hydraulic conductivity) index is usually applied to assess intrinsic vulnerability in detrital and carbonate aquifers, although it does not take into account the particularities of karst systems as the COP (C: Concentration of flow; O: Overlying layers above water table; P: precipitation) method does. In this paper we aim to find a reasonable correspondence between the vulnerability maps obtained using these two methods. We adapt the DRASTIC index in order to obtain reliable assessments in carbonate aquifers while maintaining its original conceptual formulation. This approach is analogous to the hypothesis of “equivalent porous medium”, which applies to karstic aquifers the numerical solution developed for detrital aquifers. We applied our novel method to the Upper Guadiana Basin, which contains both carbonate and detrital aquifers. Validation analysis demonstrated a higher confidence in the vulnerability assessment provided by the COP method in the carbonate aquifers. The proposed method solves an optimization problem to minimize the differences between the assessments provided by the modified DRASTIC and COP methods. Decision trees and spatial statistics analyses were combined to identify the ranges and weights of DRASTIC parameters to produce an optimal solution that matches the COP vulnerability classification for carbonate aquifers in 75% of the area, while maintaining a reliable assessment of the detrital aquifers in the Basin.

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Section: Discussionmentioning

confidence: 86%

A Novel Approach to Harmonize Vulnerability Assessment in Carbonate and Detrital Aquifers at Basin Scale

Baena-Ruíz

Pulido-Velázquez

2020

Water

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“…For example, Armengol et al (2014) explored uncertainty within DRASTIC data layers using various geostatistical techniques and analyzed the outcomes by the concept of the degree of confidence. Dizaji et al (2020) successfully used the Monte Carlo uncertainty analysis method to detect the reliability of the vulnerable areas against groundwater pollution.…”

Section: Discussionmentioning

confidence: 99%

Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

Barzegar

Razzagh

Quilty

et al. 2021

Journal of Hydrology

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“…Several aspects of human life such as hygiene, drinking, and domestic activities need clean water supply (Khalil et al 2019;Liu & Lu 2014). Unsustainable human activities have contributed immensely to the pollution of water bodies and caused high stress on freshwater resources (Dizaji et al 2020a;Zhao et al 2019). The deterioration of water quality (WQ) has severely affected humans well-being and the environment across the globe (Dizaji et al 2020b;Ouyang 2005).…”

Section: Research Backgroundmentioning

confidence: 99%

Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia

Salih

Alakili

Beyaztaş

et al. 2020

Environ Dev Sustain

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In this research, three water quality (WQ) indexes, namely dissolved oxygen (DO), biochemical oxygen demand (BOD), and chemical oxygen demand (COD), in Selangor River of peninsular Malaysia were simulated using a stochastic model based on vector autoregression (VAR). The simulation was adopted based on three modeling scenarios of inputs as predictor: (i) related WQ parameters, (ii) WQ parameters and river flow data, and (iii) WQ parameters and rainfall data. The WQ parameters as input were determined based on the correlation analysis. The numerical analyses revealed that the prediction accuracy of VAR model substantially increases with the increase in input number. The model provided better accuracy in predictions of WQ indexes (root mean square error ≈ 0.11 and mean absolute error ≈ 0.26) when all environmental, hydrological, and climatological variables were considered. Further improvement in model performance (root mean square error ≈ 0.0248 and mean absolute error ≈ 0.1259) can be achieved if physiochemical parameters like suspended solid material and the turbidity are used as additional inputs.

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Groundwater contamination vulnerability assessment using DRASTIC method, GSA, and uncertainty analysis

Cited by 14 publications

References 41 publications

A Novel Approach to Harmonize Vulnerability Assessment in Carbonate and Detrital Aquifers at Basin Scale

A Novel Approach to Harmonize Vulnerability Assessment in Carbonate and Detrital Aquifers at Basin Scale

Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models

Prediction of dissolved oxygen, biochemical oxygen demand, and chemical oxygen demand using hydrometeorological variables: case study of Selangor River, Malaysia

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