{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:16:15Z","timestamp":1778757375769,"version":"3.51.4"},"reference-count":85,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash\u2013Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.<\/jats:p>","DOI":"10.3390\/s20205763","type":"journal-article","created":{"date-parts":[[2020,10,14]],"date-time":"2020-10-14T21:24:39Z","timestamp":1602710679000},"page":"5763","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6605-498X","authenticated-orcid":false,"given":"Shahab S.","family":"Band","sequence":"first","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"},{"name":"Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saeid","family":"Janizadeh","sequence":"additional","affiliation":[{"name":"Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-8007","authenticated-orcid":false,"given":"Subodh Chandra","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3617-6820","authenticated-orcid":false,"given":"Indrajit","family":"Chowdhuri","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaleh","family":"Siabi","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran 14115-111, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akbar","family":"Norouzi","sequence":"additional","affiliation":[{"name":"Department of Natural Engineering, Faculty of Natural Resources and Earth Science, Shahrekord Unversity, Shahrekord 8818634141, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4724-9367","authenticated-orcid":false,"given":"Assefa M.","family":"Melesse","sequence":"additional","affiliation":[{"name":"Department of Earth and Environment, AHC-5-390, Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manouchehr","family":"Shokri","sequence":"additional","affiliation":[{"name":"Faculty of Civil Engineering, Institute of Structural Mechanics, Bauhaus-Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amirhosein","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam"},{"name":"Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.jhydrol.2014.02.053","article-title":"Application of GIS based data driven evidential belief function model to predict groundwater potential zonation","volume":"513","author":"Nampak","year":"2014","journal-title":"J. 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