# Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach
## 4 Conclusions
(2018) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms. _Sci. Total Environ._**2018**, _621_, 697-712. [CrossRef] [PubMed]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] [PERSON] Large scale prediction of groundwater nitrate concentrations from spatial data using machine learning. _Sci. Total Environ._**2019**, _668_, 1317-1327. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; et al. Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEC methods. _Sci. Total Environ._**2019**, _688_, 855-866. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] Construction of environmental risk score beyond standard linear models using machine learning methods: Application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. _Environ. Health_**2017**, _16_, 1-17. [CrossRef]
* [PERSON] and [PERSON] (2021) [PERSON]; [PERSON] A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities. _Neural Comput. Appl._**2021**, _14_, 1-22. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Applications of machine learning methods for engineering risk assessment--A review. _Saf. Sci._**2020**, _122_, 104492. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Prediction of bioconcentration factors in fish and invertebrates using machine learning. _Sci. Total Environ._**2019**, _648_, 80-89. [CrossRef] [PubMedMed]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON]; [PERSON] Machine learning for environmental monitoring. _Nat. Sustain._**2018**, \(1\), 583-588. [CrossRef]
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Groundwater risk assessment based on optimization framework using DRASTIC method. _Arab. J. Gcosci._**2016**, \(9\), 1-14. [CrossRef]
* [PERSON] (1985) [PERSON] _DRASTIC: A Standardized System for Evaluating Ground Water Pollution Potential Using Hydrogeologic Settings_; [PERSON] Kerr Environmental Research Laboratory: Ada, OK, USA, 1985. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON] A modified-DRASTIC model (DRASTICA) for assessment of groundwater vulnerability to pollution in an urbanized environment in Lucknow, India. _Environ. Earth Sci._**2015**, _74_, 5475-5490. [CrossRef]
* [PERSON] (2008) [PERSON] A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India. _Appl. Geogr._**2008**, _28_, 32-53. [CrossRef]
* [PERSON] et al. (2012) [PERSON]; [PERSON]; [PERSON] Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool-a case of predicting potential zones of sustainable groundwater resources. _J. Hydrol._**2012**, _440_, 75-89. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS. Environ. _Monit. Assess._**2018**, _190_, 149. [CrossRef]
* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON]; [PERSON] Groundwater productivity potential mapping using evidential belief function. _Groundwater_**2014**, _52_, 201-207. [CrossRef]
* [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] Application of Dempster-Shafer theory, spatial analysis and remote sensing for groundwater potentially and nitrate pollution analysis in the semi-arid region of Kluzestan, Iran. _Sci. Total Environ._**2016**, _568_, 1110-1123. [CrossRef] [PubMed]* _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Spatial prediction of soil erosion susceptibility using a fuzzy analytical network process: Application of the fuzzy decision making trial and evaluation laboratory approach. _Land Degrad.