Journal
SCIENCE OF THE TOTAL ENVIRONMENT
Volume 644, Issue -, Pages 954-962Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.scitotenv.2018.07.054
Keywords
Groundwater pollution; Nitrate; Probability; Risk; Vulnerability; GIS
Categories
Funding
- Marie-Curie Innovation Training Network REMEDIATE: Improved decision-making in contaminated land site investigation and risk assessment (European Union's Horizon 2020 Programme for research, technological development and demonstration) [643087]
Ask authors/readers for more resources
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 methodwas 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 approachwas applied for production of the groundwater pollution occurrence probabilitymap. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80% 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. (C) 2018 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available