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

Adiat, 2012, 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., 440, 75, 10.1016/j.jhydrol.2012.03.028 Akbar, 2011, Development and evaluation of GIS-based ArcPRZM-3 system for spatial modeling of groundwater vulnerability to pesticide contamination, Comput. Geosci., 37, 822, 10.1016/j.cageo.2011.01.011 Aller, 1987 Amiri, 2014, Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran, Environ. Earth Sci., 72, 3479, 10.1007/s12665-014-3255-0 Anane, 2013, GIS-based DRASTIC, pesticide DRASTIC and the susceptibility index (SI): comparative study for evaluation of pollution potential in the Nabeul-Hammamet shallow aquifer, Tunisia, Hydrogeol. J., 21, 715, 10.1007/s10040-013-0952-9 Arabgol, 2016, Predicting nitrate concentration and its spatial distribution in groundwater resources using support vector machines (SVMs) model, Environ. Model. Assess., 21, 71, 10.1007/s10666-015-9468-0 Bonton, 2011, Nitrate transport modeling to evaluate source water protection scenarios for a municipal well in an agricultural area, Agric. Syst., 104, 429, 10.1016/j.agsy.2011.02.001 Choubin, 2017, Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions, Environ. Earth Sci., 76, 538, 10.1007/s12665-017-6870-8 Choubin, 2017, An ensemble forecast of semi-arid rainfall using large-scale climate predictors, Meteorol. Appl., 24, 376, 10.1002/met.1635 Choubin, 2017, Watershed classification by remote sensing indices: a fuzzy c-means clustering approach, J. Mt. Sci., 14, 2053, 10.1007/s11629-017-4357-4 Choubin, 2018, River suspended sediment modelling using the CART model: a comparative study of machine learning techniques, Sci. Total Environ., 615, 272, 10.1016/j.scitotenv.2017.09.293 Choubin, 2018, Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches, Environ. Earth Sci., 77, 314, 10.1007/s12665-018-7498-z Civita, 1995 Cortes, 1995, Support-vector networks, Mach. Learn., 20, 273, 10.1007/BF00994018 Dewan, 2013 Dietterich, 2000, Ensemble methods in machine learning, 1 Dongol, 2005, Shallow groundwater in a middle mountain catchment of Nepal: quantity and quality issues, Environ. Geol., 49, 219, 10.1007/s00254-005-0064-5 Elith, 2006, Novel methods improve prediction of species' distributions from occurrence data, Ecography, 129, 10.1111/j.2006.0906-7590.04596.x Elith, 2008, A working guide to boosted regression trees, J. Anim. Ecol., 77, 802, 10.1111/j.1365-2656.2008.01390.x Esmaeili, 2014, Nitrate contamination in irrigation groundwater, Isfahan, Iran, Environ. Earth Sci., 72, 2511, 10.1007/s12665-014-3159-z Fontaine, 1992, The role of sensitivity analysis in groundwater risk modeling for pesticides, Weed Technol., 716, 10.1017/S0890037X00036101 Foster, 1987, Fundamental concepts in aquifer vulnerability, pollution risk and protection strategy, Vol. 38, 69 Garnier, 1998, Integrated use of GLEAMS and GIS to prevent groundwater pollution caused by agricultural disposal of animal waste, Environ. Manag., 22, 747, 10.1007/s002679900144 Ghorbani Nejad, 2017, Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models, Geocarto Int., 32, 167 Golkarian, 2018, Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS, Environ. Monit. Assess., 190, 149, 10.1007/s10661-018-6507-8 Hair, 1998, 5, No. 3, 207 Hutchins, 2018, Combined impacts of future land-use and climate stressors on water resources and quality in groundwater and surface waterbodies of the upper Thames river basin, UK, Sci. Total Environ., 631, 962, 10.1016/j.scitotenv.2018.03.052 Iqbal, 2012, Approaches to groundwater vulnerability to pollution: a literature review, Asian J. Water Environ. Pollut., 9, 105 Iranian Ministry of Energy (IMOF), 2014 Iranian Ministry of Energy (MOE), 1985 Jódar, 2014, Exact analytical solution of the convolution integral for classical hydrogeological lumped-parameter models and typical input tracer functions in natural gradient systems, J. Hydrol., 519, 3275, 10.1016/j.jhydrol.2014.10.027 Johnson, 2009, Assigning land use to supply wells for the statistical characterization of regional groundwater quality: correlating urban land use and VOC occurrence, J. Hydrol., 370, 100, 10.1016/j.jhydrol.2009.02.056 Kazakis, 2015, Groundwater vulnerability and pollution risk assessment of porous aquifers to nitrate: modifying the DRASTIC method using quantitative parameters, J. Hydrol., 525, 13, 10.1016/j.jhydrol.2015.03.035 Khalil, 2005, Applicability of statistical learning algorithms in groundwater quality modeling, Water Resour. Res., 41, W05010, 10.1029/2004WR003608 Lee, 2012, Ensemble-based landslide susceptibility maps in Jinbu area, Korea, Environ. Earth Sci., 67, 23, 10.1007/s12665-011-1477-y Lee, 2012, Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping, J. Environ. Manag., 96, 91, 10.1016/j.jenvman.2011.09.016 Leonard, 1987, GLEAMS: groundwater loading effects of agricultural management systems, Trans. ASAE, 30, 1403, 10.13031/2013.30578 Leone, 2009, Vulnerability and risk evaluation of agricultural nitrogen pollution for Hungary's main aquifer using DRASTIC and GLEAMS models, J. Environ. Manag., 90, 2969, 10.1016/j.jenvman.2007.08.009 Majolagbe, 2016, Vulnerability assessment of groundwater pollution in the vicinity of an active dumpsite (Olusosun), Lagos, Nigeria, Chem. Int., 2, 232 Martínez-Bastida, 2010, Intrinsic and specific vulnerability of groundwater in central Spain: the risk of nitrate pollution, Hydrogeol. J., 18, 681, 10.1007/s10040-009-0549-5 Matzeu, 2017, Methodological approach to assessment of groundwater contamination risk in an agricultural area, Agric. Water Manag., 184, 46, 10.1016/j.agwat.2017.01.003 McLay, 2001, Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches, Environ. Pollut., 115, 191, 10.1016/S0269-7491(01)00111-7 Monserud, 1992, Comparing global vegetation maps with the Kappa statistic, Ecol. Model., 62, 275, 10.1016/0304-3800(92)90003-W Naimi, 2016, Sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39, 368, 10.1111/ecog.01881 Narany, 2014, Assessment of the potential contamination risk of nitrate in groundwater using indicator kriging (in Amol–Babol Plain, Iran), 273 Neshat, 2015, An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment, Nat. Hazards, 76, 543, 10.1007/s11069-014-1503-y Neshat, 2014, Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran, Environ. Earth Sci., 71, 3119, 10.1007/s12665-013-2690-7 Neshat, 2015, Risk assessment of groundwater pollution using Monte Carlo approach in an agricultural region: an example from Kerman Plain, Iran, Comput. Environ. Urban. Syst., 50, 66, 10.1016/j.compenvurbsys.2014.11.004 Nobre, 2007, Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool, J. Contam. Hydrol., 94, 277, 10.1016/j.jconhyd.2007.07.008 Ozdemir, 2011, Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey), J. Hydrol., 405, 123, 10.1016/j.jhydrol.2011.05.015 Park, 2014, Groundwater productivity potential mapping using evidential belief function, Groundwater, 52, 201, 10.1111/gwat.12197 Pourghasemi, 2017, Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling, Sci. Total Environ., 609, 764, 10.1016/j.scitotenv.2017.07.198 Qin, 2013, Assessing the impact of natural and anthropogenic activities on groundwater quality in coastal alluvial aquifers of the lower Liaohe River Plain, NE China, Appl. Geochem., 31, 142, 10.1016/j.apgeochem.2013.01.001 Rahman, 2008, A GIS based DRASTIC model for assessing groundwater vulnerability in shallow aquifer in Aligarh, India, Appl. Geogr., 28, 32, 10.1016/j.apgeog.2007.07.008 Rahmati, 2016, Application of Dempster–Shafer theory, spatial analysis and remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of Khuzestan, Iran, Sci. Total Environ., 568, 1110, 10.1016/j.scitotenv.2016.06.176 Razandi, 2015, Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS, Earth Sci. Inf., 8, 867, 10.1007/s12145-015-0220-8 Rokach, 2010, Ensemble-based classifiers, Artif. Intell. Rev., 33, 1, 10.1007/s10462-009-9124-7 Sajedi-Hosseini, 2018, Spatial prediction of soil erosion susceptibility using FANP: application of the Fuzzy DEMATEL approach, Land Degrad. Dev., 10.1002/ldr.3058 Schapire, 2003, The boosting approach to machine learning: an overview, Nonlinear Estimation Classif., 171, 149, 10.1007/978-0-387-21579-2_9 Shrestha, 2016, Assessment of groundwater vulnerability and risk to pollution in Kathmandu Valley, Nepal, Sci. Total Environ., 556, 23, 10.1016/j.scitotenv.2016.03.021 Singh, 2018, Developing robust arsenic awareness prediction models using machine learning algorithms, J. Environ. Manag., 211, 125, 10.1016/j.jenvman.2018.01.044 Stigter, 2006, Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal, Hydrogeol. J., 14, 79, 10.1007/s10040-004-0396-3 Van Beynen, 2012, Comparative study of specific groundwater vulnerability of a karst aquifer in central Florida, Appl. Geogr., 32, 868, 10.1016/j.apgeog.2011.09.005 Vladimir, 1995 Voudouris, 2010, Assessment of intrinsic vulnerability using DRASTIC model and GIS in Kiti aquifer, Cyprus World Health Organization, 2011 Xie, 2011, Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies, Qual. Quant., 45, 671, 10.1007/s11135-010-9376-y Xu, 2016, Joint identification of contaminant source location, initial release time, and initial solute concentration in an aquifer via ensemble Kalman filtering, Water Resour. Res., 52, 6587, 10.1002/2016WR019111 Yesilnacar, 2005 Zhou, 2010, DRAV model and its application in assessing groundwater vulnerability in arid area: a case study of pore phreatic water in Tarim Basin, Xinjiang, Northwest China, Environ. Earth Sci., 60, 1055, 10.1007/s12665-009-0250-y