Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: Modifying the DRASTIC method using quantitative parameters

Kazakis, Nerantzis; Voudouris, K.

doi:10.1016/j.jhydrol.2015.03.035

Cited by 263 publications

(109 citation statements)

References 29 publications

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“…Vulnerability and contamination risk maps of groundwater constitute important tools for groundwater management and protection (Kazakis and Voudouris 2015). Vulnerability assessment of groundwater is not a characteristic that can be directly measured in the field.…”

Section: Introductionmentioning

confidence: 99%

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze NO À 3 concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the NO À 3 data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using NO À 3 concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and NO À 3 concentration values. After model training, the AI models are verified by the second NO À 3 concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.

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Section: Introductionmentioning

confidence: 99%

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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show abstract

“…The need to add additional variables to increase the efficiency of method has been realized in many studies. Several studies have modified the DRASTIC variables by adding landuse variables [14,31] and calibrating the point rating of variables based on correlation of nitrate concentration and hydrological variables to the model or modified the rating values to increase the efficiency of the method [32]. Some authors have also emphasized on the availability and reliability of data suitable for the DRASTIC methods and interdependence of variable in linear combination could cause uncertainty in the method [30].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods

Shrestha

Luo

2018

IJGI

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Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses hydrogeologic conditions to predict vulnerable areas. This paper presents a new Geodetector-based Frequency Ratio (GFR) method and an optimized-DRASTIC method to generate nitrate vulnerability index values for the CV. The optimized-DRASTIC method combined the individual weights and rating values for Depth to water, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity. The GFR method incorporated the Frequency-Ratio (FR) method to derive rating values and the Geodetector method to derive relative Power of Determinant (PD) values as weights to generate nitrate susceptibility index map. The optimized-DRASTIC method generated very-high to high index values in the eastern part of the CV. The GFR method showed very-high index values in most part of the San Joaquin and Tulare basin. The quantitatively derived rating values and weights in the GFR method improved the vulnerability index and showed better consistency with the observed nitrate contamination pattern than optimized-DRASTIC index, suggesting that GFR is a better method for groundwater contamination vulnerability mapping in the CV aquifer.

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“…Since the 1980s, various models and approaches for vulnerability assessment and mapping have been developed and tested [2][3]. Groundwater vulnerability can be defi ned by intrinsic vulnerability or specifi c vulnerability [4]. The intrinsic vulnerability of an aquifer is the capacity with which a contaminant introduced at the ground surface can reach and be diffused in groundwater [5].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the DRASTIC Method for Nitrate Pollution in Groundwater Vulnerability Assessments: a Case Study in China

He¹,

Li²,

Li³

et al. 2018

Pol. J. Environ. Stud.

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Groundwater vulnerability assessments, using DRASTIC, are important and useful tools for groundwater pollution prevention and control. The DRASTIC method, however, is not appropriate for accurate specifi c vulnerability assessments where nitrate concentrations are high. A new method has been developed that retains the basic structure of DRASTIC while adding or subtracting parameters, and modifying the parameter ratings and weightings. The resulting DRACILM model was used to assess vulnerability to nitrate pollution in the West Liaohe Plain and as a basis for vulnerability mapping. The accuracy, appropriateness, and reliability of the vulnerability mapping were analyzed using a group of integrated indicators, such as correlation, ANOVA F-statistics, and single-parameter sensitivity analysis. The correlation between vulnerability class and the concentration of NO 3 -N in the DRACILM model improved to 0.649, which was 40.6% higher than that obtained by DRASTIC. The ANOVA F-statistic was 27.71, which indicated a lower overlap between the mean values of nitrates in the different vulnerability classes. The single-parameter sensitivity analysis revealed that land use type exhibited the highest and hydraulic conductivity the lowest effective weighting values. The vulnerability maps by DRACILM model could assist planners and government decision-makers with preliminary investigations into planning water protection projects or establishing management scenarios for water resource quality.

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Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: Modifying the DRASTIC method using quantitative parameters

Cited by 263 publications

References 29 publications

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods

Optimizing the DRASTIC Method for Nitrate Pollution in Groundwater Vulnerability Assessments: a Case Study in China

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