Sensitivity analysis of groundwater vulnerability using DRASTIC method: A case study of National Capital Territory, Delhi, India

Tomer, Tapasya; Katyal, Deeksha; Joshi, Varun

doi:10.1016/j.gsd.2019.100271

Cited by 39 publications

(16 citation statements)

References 37 publications

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“…The ability of the aquifer to transfer water that is constantly flowing in it is called hydraulic conductivity (Saha and Alam, 2014). Aquifers with the highest levels of hydraulic conductivity are most at risk of contamination (Tomer et al, 2019).The value of hydraulic conductivity ranged between 4.32 and 43.2 m/day, for the study area.…”

Section: Discussionmentioning

confidence: 99%

“…For areas with deeper water, more time is also available for pollution to decrease due to transformation or degradation of contaminants (Maqsoom et al, 2020). The classification of the depth to water table was 1 (minimum impact) to 10 (maximum impact) for input into DRASTIC.In general, deeper values correspond with lower DRASTIC rank values (Abdelmadjid and Omar, 2013;Natraj and Katyal, 2014;Ghosh et al, 2015;Tomer et al, 2019).To prepare this layer, data related to 18 piezometers, located in different places of the plain, were collected through the regional water of southern-Khorasan Province, in a one-year period from October 2017 to September 2018.ArcGIS 10.6 and interpolated using Kriging interpolation method in Spatial Analyst extension (Eftekhari et al, 2019;Tomer et al, 2019) (Fig. 2A).…”

Section: Drastic Modelmentioning

confidence: 99%

“…In these areas, runoff is less, and the possibility of pollutant infiltration increases. Areas with steep slopes have less infiltration and large amounts of runoff and are therefore lessvulnerable to groundwater pollution (Tomer et al, 2019). A slope map was prepared using DEM with a spatial resolution of 30 meters in ArcGIS 10.6 ( Fig.…”

Section: Topographymentioning

confidence: 99%

“…Hydraulic conductivity refers to the ability of the aquifer to control the transfer of material. Aquifers with the highest hydraulic conductivity are most at risk of contamination (Tomer et al, 2019). To prepare this layer, Transmissivity data and information frompumping tests were obtainedfrom the Regional Water Department of southern Khorasan Province.…”

Section: Hydraulic Conductivitymentioning

confidence: 99%

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Anthropic Pollution Impacts on Groundwater Vulnerability Based on Modified DRASTIC-FAHP

Goodarzi

Niknam

Jamali

et al. 2021

Preprint

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In arid and semi-arid regions such as Iran, groundwater is more important for humans and ecosystems than surface water. Different models of groundwater vulnerability assessment can be used to better manage water resources. The purpose of this study is to evaluate the qualitative vulnerability of groundwater resources in the Birjand Plain aquifer using the DRASTIC model and 7 hydrogeological components. DRASTIC model was also modified by adding a land use component (MDRASTIC) based on Analytical Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) methods. After calculating the vulnerability index, the vulnerability of each method was mapped and the final index obtained from each method was classified into 4 different categories. Nitrate concentration was used to confirm the results and to analyze the sensitivity of a single parameter. Sensitivity analysis showed that the groundwater vulnerability is mainly affected by water depth and land use. To validate each of the models, their correlation with nitrate concentration was calculated and compared. To determine the correlation coefficient, simple linear regression method was performed and the Pearson and Spearman methods were used. According to the obtained Pearson correlation results, the DRASTIC, MDRASTIC, MDRASTIC-AHP, and MDRASTIC-FAHP models resulted in values of 0.550, 0.680, 0.778, and 0.794respectively. The results show a good correlation between the modified DRASTIC-FAHP model and nitrate concentration as an indicator of groundwater pollution.

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Section: Discussionmentioning

confidence: 99%

Section: Drastic Modelmentioning

confidence: 99%

Section: Topographymentioning

confidence: 99%

Section: Hydraulic Conductivitymentioning

confidence: 99%

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Anthropic Pollution Impacts on Groundwater Vulnerability Based on Modified DRASTIC-FAHP

Goodarzi

Niknam

Jamali

et al. 2021

Preprint

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“…Sensitivity analysis (SA) is able to quantify the importance of input parameters for output parameters. SA is widely used in various fields such as in maritime related fields [19,20], environmental pollution [21], and renewable energy [22]. Although SA has been widely used in various fields, there are few reports on the application of SA in thermomechanical processing.…”

Section: Introductionmentioning

confidence: 99%

An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN)

Cai

Wei

et al. 2020

Materials

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Based on an 33Cr23Ni8Mn3N thermal simulation experiment, the application of an artificial neural network (ANN) in thermomechanical processing was studied. Based on the experimental data, a microstructure evolution model and constitutive equation of 33Cr23Ni8Mn3N heat-resistant steel were established. Stress, dynamic recrystallization (DRX) fraction, and DRX grain size were predicted. These models were evaluated by a variety of statistical indicators to determine that these models would work well if applied in predicting microstructure evolution and that they have high precision. Then, based on the weight of the ANN model, the sensitivity of the input parameters was analyzed to achieve an optimized ANN model. Based on the most widely used sensitivity analysis (SA) method (the Garson method), the input parameters were analyzed. The results show that the most important factor for the microstructure of 33Cr23Ni8Mn3N is the strain rate ( ε ˙ ). For the control of the microstructure, the control of the ε ˙ is preferred. ANN was applied to the development of processing map. The feasibility of the ANN processing map on austenitic heat-resistant steel was verified by experiments. The results show that the ANN processing map is basically consistent with processing map based on experimental data. The trained ANN model was implanted into finite element simulation software and tested. The test results show that the ANN model can accurately expand the data volume to achieve high precision simulation results.

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Assessment of groundwater vulnerability to nitrates using the GIS-based DRASTIC and SI methods: a case study in Zacharo area, Greece

Panagopoulos

Κατσάνου

Barouchas

2023

Environ Monit Assess

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Sensitivity analysis of groundwater vulnerability using DRASTIC method: A case study of National Capital Territory, Delhi, India

Cited by 39 publications

References 37 publications

Anthropic Pollution Impacts on Groundwater Vulnerability Based on Modified DRASTIC-FAHP

Anthropic Pollution Impacts on Groundwater Vulnerability Based on Modified DRASTIC-FAHP

An Investigation into the Dynamic Recrystallization (DRX) Behavior and Processing Map of 33Cr23Ni8Mn3N Based on an Artificial Neural Network (ANN)

Assessment of groundwater vulnerability to nitrates using the GIS-based DRASTIC and SI methods: a case study in Zacharo area, Greece

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