GIS-based DRASTIC, Pesticide DRASTIC and the Susceptibility Index (SI): comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer, Tunisia

Anane, Makram; Abidi, Bessem; Lachaal, Fethi; Limam, Atef; Jellali, Salah

doi:10.1007/s10040-013-0952-9

Cited by 75 publications

(29 citation statements)

References 43 publications

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“…The fourth and most complex approach is process based models such as ground-water flow model (MODFLOW) (Nobre et al, 2007), water flow and nitrate transport global model (WNGM) (Bonton et al, 2011;Qin et al, 2013), pesticide root zone model (PRZM-3) (Fontaine et al, 1992;Akbar at al., 2011), groundwater loading effects of agricultural management systems (GLEAMS) (Leone et al, 2009;Leonard et al, 1987). The main weaknesses associated with these models are (i) the need for large input data (Iqbal et al, 2012), and (ii) the limited regional scales applicability (Garnier et al, 1998;Anane et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Hosseini

Malekian

Choubin

et al. 2018

Science of The Total Environment

271

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This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80 percent were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.

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Section: Introductionmentioning

confidence: 99%

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Hosseini

Malekian

Choubin

et al. 2018

Science of The Total Environment

271

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“…Within this context, the Susceptibility index (SI), developed by Ribeiro [21], is a contemporary adaptation of the Drastic method and has also been applied in this study to assess the effect of the land use on groundwater vulnerability in an agricultural coastal plain where adverse land use changes are common [22]. The Sustainability…”

Section: Introductionmentioning

confidence: 99%

Assessment of groundwater contamination risk in an agricultural area in north Italy

Bartzas

Tinivella²,

Medini³

et al. 2015

Information Processing in Agriculture

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In the present study a specific approach is followed, considering the Pesticide DRASTIC and Susceptibility Index (SI) methods and a GIS framework, to assess groundwater vulnerability in the agricultural area of Albenga, in north Italy. The results indicate "high" to "very high" vulnerability to groundwater contamination along the coastline and the middle part of the Albenga plain, for almost 49% and 56% of the total study area for Pesticide DRASTIC and SI methods, respectively. These sensitive regions depict characteristics such as shallow depth to groundwater, extensive deposits of alluvial silty clays, flat topography and intensive agricultural activities. The distribution of nitrates concentration in groundwater in the study area is slightly better correlated with the SI (0.728) compared to Pesticide DRASTIC (0.693), thus indicating that both methods are characterized by quite good accuracy. Sensitivity analysis was also performed to acknowledge statistical uncertainty in the estimation of each parameter used, assess its impact and thus identify the most critical parameters that require further investigation in the future. Depth to water is the parameter that exhibited the largest impact on the Pesticide DRASTIC vulnerability index followed by the impact of the vadose zone and topography. On the other hand, the SI method is more sensitive to the removal of the topography parameter followed by the aquifer media and the depth to water parameters.

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“…Pesticide DRASTIC does not consider the current land use but rather the risk of pollution if agricultural practises are carried out (Annae et al, 2013). In the vulnerability map arrived from modified Pesticide DRASTIC, the area of very highly vulnerable zone was more compared to Pesticide DRASTIC.…”

Section: Aquifer Vulnerability Mapmentioning

confidence: 99%

Cross comparison of five popular groundwater pollution vulnerability index approaches

Brindha

Elango

2015

Journal of Hydrology

101

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GIS-based DRASTIC, Pesticide DRASTIC and the Susceptibility Index (SI): comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer, Tunisia

Cited by 75 publications

References 43 publications

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination

Assessment of groundwater contamination risk in an agricultural area in north Italy

Cross comparison of five popular groundwater pollution vulnerability index approaches

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