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Volumn 2, Issue 1, 2016, Pages

Simulation of nitrate contamination in groundwater using artificial neural networks

Author keywords

Groundwater; Modeling; Neural networks; Pollution

Indexed keywords

ARTIFICIAL NEURAL NETWORK; GROUNDWATER POLLUTION; HYDROGEOLOGY; MODELING; NITRATE; SOIL CARBON; SOIL NITROGEN; SOIL ORGANIC MATTER;

EID: 85048716490     PISSN: 23636203     EISSN: 23636211     Source Type: Journal    
DOI: 10.1007/s40808-016-0080-3     Document Type: Article
Times cited : (37)

References (39)
  • 1
    • 0031549460 scopus 로고    scopus 로고
    • Nitrate concentrations in Riyadh, Saudi Arabia drinking water supplies
    • Alabdula’aly AI (1997) Nitrate concentrations in Riyadh, Saudi Arabia drinking water supplies. Environ Monit Assess 47(3):315–324 DOI: 10.1023/A:1005756904710
    • (1997) Environ Monit Assess , vol.47 , Issue.3 , pp. 315-324
    • Alabdula’aly, A.I.1
  • 4
    • 0022266419 scopus 로고
    • Modelling non-point sources of nitrate pollution of groundwater in the Great Ouse Chalk, U.K
    • Carey MA, Lloyd JW (1985) Modelling non-point sources of nitrate pollution of groundwater in the Great Ouse Chalk, U.K. J Hydrol 78(1–2):83–106 DOI: 10.1016/0022-1694(85)90155-6
    • (1985) J Hydrol , vol.78 , Issue.1-2 , pp. 83-106
    • Carey, M.A.1    Lloyd, J.W.2
  • 5
    • 77954151133 scopus 로고    scopus 로고
    • Validation of an artificial neural network model for landslide susceptibility mapping
    • Choi J, Oh HJ, Won JS, Lee S (2010) Validation of an artificial neural network model for landslide susceptibility mapping. Environ Earth Sci 60(3):473–483 DOI: 10.1007/s12665-009-0188-0
    • (2010) Environ Earth Sci , vol.60 , Issue.3 , pp. 473-483
    • Choi, J.1    Oh, H.J.2    Won, J.S.3    Lee, S.4
  • 6
    • 85050933599 scopus 로고    scopus 로고
    • Determination of nitrate concentration in groundwater in agricultural area in Babol County
    • Ehteshami M, Biglarijoo N (2014) Determination of nitrate concentration in groundwater in agricultural area in Babol County. Iran J Health Sci 2(4):1–9
    • (2014) Iran J Health Sci , vol.2 , Issue.4 , pp. 1-9
    • Ehteshami, M.1    Biglarijoo, N.2
  • 7
    • 84929157926 scopus 로고    scopus 로고
    • Simulation of nitrate contamination in groundwater caused by livestock industry (case study: Rey)
    • Ehteshami M, Sefidkar Langeroudi A, Tavassoli S (2013) Simulation of nitrate contamination in groundwater caused by livestock industry (case study: Rey). J Environ Prot 4(7A):91–97 DOI: 10.4236/jep.2013.47A011
    • (2013) J Environ Prot , vol.4 , Issue.7A , pp. 91-97
    • Ehteshami, M.1    Sefidkar Langeroudi, A.2    Tavassoli, S.3
  • 8
    • 0003638838 scopus 로고
    • Managing nitrogen for groundwater quality and farm profitability: Overview and introduction
    • Follett RF, Kenney DR, Cruse RM, Proceedings of a symposium. Soil Science Society of America, Madison, Wisconsin
    • Follett RF, Keeney DR, Cruse RM (1991) Managing nitrogen for groundwater quality and farm profitability: overview and introduction. In: Follett RF, Kenney DR, Cruse RM (eds) Managing nitrogen for groundwater quality and farm profitability. Proceedings of a symposium. Soil Science Society of America, Madison, Wisconsin, pp 1–7
    • (1991) Managing Nitrogen for Groundwater Quality and Farm Profitability , pp. 1-7
    • Follett, R.F.1    Keeney, D.R.2    Cruse, R.M.3
  • 10
    • 84890408305 scopus 로고    scopus 로고
    • Nitrogen speciation and trends, and prediction of denitrification extent, in shallow US groundwater
    • Hinkle SR, Tesoriero AJ (2014) Nitrogen speciation and trends, and prediction of denitrification extent, in shallow US groundwater. J Hydrol 509:343–353 DOI: 10.1016/j.jhydrol.2013.11.048
    • (2014) J Hydrol , vol.509 , pp. 343-353
    • Hinkle, S.R.1    Tesoriero, A.J.2
  • 11
    • 79956061202 scopus 로고    scopus 로고
    • Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method
    • Huang J, Xu J, Liu X, Liu J, Wang L (2011) Spatial distribution pattern analysis of groundwater nitrate nitrogen pollution in Shandong intensive farming regions of China using neural network method. Math Comput Model 54(3):995–1004 DOI: 10.1016/j.mcm.2010.11.027
    • (2011) Math Comput Model , vol.54 , Issue.3 , pp. 995-1004
    • Huang, J.1    Xu, J.2    Liu, X.3    Liu, J.4    Wang, L.5
  • 12
    • 0031106314 scopus 로고    scopus 로고
    • Strategies and best practice for neural network image classification
    • Kanellopoulos I, Wilkinson G (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18(4):711–725 DOI: 10.1080/014311697218719
    • (1997) Int J Remote Sens , vol.18 , Issue.4 , pp. 711-725
    • Kanellopoulos, I.1    Wilkinson, G.2
  • 13
    • 0026826901 scopus 로고
    • Nitrate risk management under uncertainty
    • Lee YW, Dahab MF, Bogardi I (1992) Nitrate risk management under uncertainty. J Water Resour Plan Manag 118(2):151–165 DOI: 10.1061/(ASCE)0733-9496(1992)118:2(151)
    • (1992) J Water Resour Plan Manag , vol.118 , Issue.2 , pp. 151-165
    • Lee, Y.W.1    Dahab, M.F.2    Bogardi, I.3
  • 14
    • 84990571875 scopus 로고
    • The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision
    • Lenard MJ, Alam P, Madey GR (1995) The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision. Decis Sci 26(2):209–227 DOI: 10.1111/j.1540-5915.1995.tb01426.x
    • (1995) Decis Sci , vol.26 , Issue.2 , pp. 209-227
    • Lenard, M.J.1    Alam, P.2    Madey, G.R.3
  • 15
    • 0034749386 scopus 로고    scopus 로고
    • Multicomponent simulation of wastewater-derived nitrogen and carbon in shallow unconfined aquifers: II. Model application to a field site
    • MacQuarrie KT, Sudicky EA, Robertson WD (2001) Multicomponent simulation of wastewater-derived nitrogen and carbon in shallow unconfined aquifers: II. Model application to a field site. J Contam Hydrol 47(1):85–104 DOI: 10.1016/S0169-7722(00)00138-8
    • (2001) J Contam Hydrol , vol.47 , Issue.1 , pp. 85-104
    • MacQuarrie, K.T.1    Sudicky, E.A.2    Robertson, W.D.3
  • 16
    • 0032051569 scopus 로고    scopus 로고
    • The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study
    • Maier HR, Dandy GC (1998) The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ Model Softw 13(2):193–209 DOI: 10.1016/S1364-8152(98)00020-6
    • (1998) Environ Model Softw , vol.13 , Issue.2 , pp. 193-209
    • Maier, H.R.1    Dandy, G.C.2
  • 17
    • 74849107222 scopus 로고    scopus 로고
    • A neural network based urban growth model of an Indian city
    • Maithani S (2009) A neural network based urban growth model of an Indian city. J Indian Soc Remote Sens 37(3):363–376 DOI: 10.1007/s12524-009-0041-7
    • (2009) J Indian Soc Remote Sens , vol.37 , Issue.3 , pp. 363-376
    • Maithani, S.1
  • 19
    • 85086782599 scopus 로고    scopus 로고
    • Estimating the spatial distribution ofgroundwater quality parameters of Kashan plain with integration method of geostatistics–artificial neural network optimized by genetic-algorithm
    • Moasheri SA, Rezapour OM, Beyranvand Z, Poornoori Z (2013) Estimating the spatial distribution ofgroundwater quality parameters of Kashan plain with integration method of geostatistics–artificial neural network optimized by genetic-algorithm. Int J Agric Crop Sci 5(20):2434–2442
    • (2013) Int J Agric Crop Sci , vol.5 , Issue.20 , pp. 2434-2442
    • Moasheri, S.A.1    Rezapour, O.M.2    Beyranvand, Z.3    Poornoori, Z.4
  • 20
    • 3142538909 scopus 로고    scopus 로고
    • Improved streamflow forecasting using self-organizing radial basis function artificial neural networks
    • Moradkhani H, Kl Hsu, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 205(1):246–262 DOI: 10.1016/j.jhydrol.2004.03.027
    • (2004) J Hydrol , vol.205 , Issue.1 , pp. 246-262
    • Moradkhani, H.1    Kl, H.2    Gupta, H.V.3    Sorooshian, S.4
  • 21
    • 0029415649 scopus 로고
    • A review and analysis of backpropagation neural networks for classification of remotely sensed multispectral imagery
    • Paola JD, Schowengerdt RA (1995) A review and analysis of backpropagation neural networks for classification of remotely sensed multispectral imagery. Int J Remote Sens 16(16):3033–3058 DOI: 10.1080/01431169508954607
    • (1995) Int J Remote Sens , vol.16 , Issue.16 , pp. 3033-3058
    • Paola, J.D.1    Schowengerdt, R.A.2
  • 22
    • 84990585716 scopus 로고
    • Two-group classification using neural networks
    • Patuwo E, Hu MY, Hung MS (1993) Two-group classification using neural networks. Decis Sci 24(4):825–845 DOI: 10.1111/j.1540-5915.1993.tb00491.x
    • (1993) Decis Sci , vol.24 , Issue.4 , pp. 825-845
    • Patuwo, E.1    Hu, M.Y.2    Hung, M.S.3
  • 23
    • 0028448573 scopus 로고
    • A classification approach using multi-layered neural networks
    • Piramuthu S, Shaw MJ, Gentry JA (1994) A classification approach using multi-layered neural networks. Decis Support Syst 11(5):509–525 DOI: 10.1016/0167-9236(94)90022-1
    • (1994) Decis Support Syst , vol.11 , Issue.5 , pp. 509-525
    • Piramuthu, S.1    Shaw, M.J.2    Gentry, J.A.3
  • 25
    • 0034174354 scopus 로고    scopus 로고
    • Neural Networks for Agrichemical Vulnerability Assessment of Rural Private Wells
    • Ray C, Klindworth KK (2000) Neural Networks for Agrichemical Vulnerability Assessment of Rural Private Wells. J Hydrol Eng 5(2):162–171 DOI: 10.1061/(ASCE)1084-0699(2000)5:2(162)
    • (2000) J Hydrol Eng , vol.5 , Issue.2 , pp. 162-171
    • Ray, C.1    Klindworth, K.K.2
  • 26
    • 53149117415 scopus 로고    scopus 로고
    • Nitrate attenuation in groundwater: a review of biogeochemical controlling processes
    • Rivett MO, Buss SR, Morgan P, Smith JW, Bemment CD (2008) Nitrate attenuation in groundwater: a review of biogeochemical controlling processes. Water Res 42(16):4215–4232 DOI: 10.1016/j.watres.2008.07.020
    • (2008) Water Res , vol.42 , Issue.16 , pp. 4215-4232
    • Rivett, M.O.1    Buss, S.R.2    Morgan, P.3    Smith, J.W.4    Bemment, C.D.5
  • 27
    • 13744256757 scopus 로고    scopus 로고
    • Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks
    • Sahoo G, Ray C, Wade H (2005) Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks. Ecol Model 183(1):29–46 DOI: 10.1016/j.ecolmodel.2004.07.021
    • (2005) Ecol Model , vol.183 , Issue.1 , pp. 29-46
    • Sahoo, G.1    Ray, C.2    Wade, H.3
  • 28
    • 84939429345 scopus 로고    scopus 로고
    • Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers)
    • Salami ES, Ehteshami M (2015) Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers). Int J Eng Sci Technol 12(10):3235–3242. doi:10.1007/s13762-015-0800-7
    • (2015) Int J Eng Sci Technol , vol.12 , Issue.10 , pp. 3235-3242
    • Salami, E.S.1    Ehteshami, M.2
  • 29
    • 84954518100 scopus 로고    scopus 로고
    • Application of artificial neural networks to estimating DO and salinity in San Joaquin River basin
    • Salami ES, Ehteshami M (2016) Application of artificial neural networks to estimating DO and salinity in San Joaquin River basin. Desalin Water Treat 57(11):4888–4897. doi:10.1080/19443994.2014.995713 DOI: 10.1080/19443994.2014.995713
    • (2016) Desalin Water Treat , vol.57 , Issue.11 , pp. 4888-4897
    • Salami, E.S.1    Ehteshami, M.2
  • 30
    • 0742323231 scopus 로고    scopus 로고
    • Neural networks for predicting nitrate-nitrogen in drainage water
    • Sharma V, Negi SC, Rudra RP, Yang S (2003) Neural networks for predicting nitrate-nitrogen in drainage water. Agric Water Manag 63(3):169–183 DOI: 10.1016/S0378-3774(03)00159-8
    • (2003) Agric Water Manag , vol.63 , Issue.3 , pp. 169-183
    • Sharma, V.1    Negi, S.C.2    Rudra, R.P.3    Yang, S.4
  • 31
    • 0024825509 scopus 로고
    • Nitrate pollution of groundwater in western Europe
    • Strebel O, Duynisveld W, Böttcher J (1989) Nitrate pollution of groundwater in western Europe. Agric Ecosyst Environ 26(3):189–214 DOI: 10.1016/0167-8809(89)90013-3
    • (1989) Agric Ecosyst Environ , vol.26 , Issue.3 , pp. 189-214
    • Strebel, O.1    Duynisveld, W.2    Böttcher, J.3
  • 32
    • 0344035546 scopus 로고    scopus 로고
    • Evaluation of neural networks for modeling nitrate concentrations in rivers
    • Suen J, Eheart J (2003) Evaluation of neural networks for modeling nitrate concentrations in rivers. J Water Resour Plan Manag 129(6):505–510 DOI: 10.1061/(ASCE)0733-9496(2003)129:6(505)
    • (2003) J Water Resour Plan Manag , vol.129 , Issue.6 , pp. 505-510
    • Suen, J.1    Eheart, J.2
  • 33
    • 0000596894 scopus 로고
    • Groundwater quality near two cattle feedlots in Texas High Plains: a case study
    • Sweeten J, Marek T, McReynolds D (1995) Groundwater quality near two cattle feedlots in Texas High Plains: a case study. Appl Eng Agric 11(6):845–850 DOI: 10.13031/2013.25812
    • (1995) Appl Eng Agric , vol.11 , Issue.6 , pp. 845-850
    • Sweeten, J.1    Marek, T.2    McReynolds, D.3
  • 36
    • 0028034630 scopus 로고
    • The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment
    • Wang F (1994) The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment. Environ and Plan A 26(2):265–284 DOI: 10.1068/a260265
    • (1994) Environ and Plan A , vol.26 , Issue.2 , pp. 265-284
    • Wang, F.1
  • 37
    • 84895743002 scopus 로고    scopus 로고
    • Groundwater flow path dynamics and nitrogen transport potential in the riparian zone of an agricultural headwater catchment
    • Williams MR, Buda AR, Elliott HA, Hamlett J, Boyer EW, Schmidt JP (2014) Groundwater flow path dynamics and nitrogen transport potential in the riparian zone of an agricultural headwater catchment. J Hydrol 511:870–879 DOI: 10.1016/j.jhydrol.2014.02.033
    • (2014) J Hydrol , vol.511 , pp. 870-879
    • Williams, M.R.1    Buda, A.R.2    Elliott, H.A.3    Hamlett, J.4    Boyer, E.W.5    Schmidt, J.P.6
  • 38
    • 0036010873 scopus 로고    scopus 로고
    • Reactive nitrogen and human health: acute and long-term implications
    • Wolfe AH, Patz JA (2002) Reactive nitrogen and human health: acute and long-term implications. AMBIO J Hum Environ 31(2):120–125 DOI: 10.1579/0044-7447-31.2.120
    • (2002) AMBIO J Hum Environ , vol.31 , Issue.2 , pp. 120-125
    • Wolfe, A.H.1    Patz, J.A.2
  • 39
    • 0033107134 scopus 로고    scopus 로고
    • Applications of neural networks to simulate soil-tool interaction and soil behavior
    • Zhang ZX, Kushwaha RL (1999) Applications of neural networks to simulate soil-tool interaction and soil behavior. Can Agric Eng 41(2):119–125
    • (1999) Can Agric Eng , vol.41 , Issue.2 , pp. 119-125
    • Zhang, Z.X.1    Kushwaha, R.L.2


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