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Volumn 12, Issue 10, 2015, Pages 3235-3242

Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers)

Author keywords

Artificial neural networks; Ireland Rivers; Modeling; Water characteristics; Water quality

Indexed keywords

ALKALINITY; BIOCHEMICAL OXYGEN DEMAND; CHLORINE COMPOUNDS; DISSOLVED OXYGEN; MODELS; NEURAL NETWORKS; RIVERS; WATER QUALITY;

EID: 84939429345     PISSN: 17351472     EISSN: 17352630     Source Type: Journal    
DOI: 10.1007/s13762-015-0800-7     Document Type: Article
Times cited : (37)

References (35)
  • 1
    • 79957466802 scopus 로고    scopus 로고
    • Oklahoma State University, Stillwater
    • Abraham A (2005) Artificial neural networks. Oklahoma State University, Stillwater, pp 901–908
    • (2005) Artificial neural networks , pp. 901-908
    • Abraham, A.1
  • 2
    • 84939439987 scopus 로고    scopus 로고
    • Prediction of BOD values in engineering work industrial effluent by Anfis modeling
    • Akilandeswari S, Adline MH (2013) Prediction of BOD values in engineering work industrial effluent by Anfis modeling. Int J Res Pure Appl Phys 3(2):7–9
    • (2013) Int J Res Pure Appl Phys , vol.3 , Issue.2 , pp. 7-9
    • Akilandeswari, S.1    Adline, M.H.2
  • 3
    • 60949114528 scopus 로고    scopus 로고
    • A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment
    • Anctila F, Filion M, Tournebizeb J (2009) A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment. Ecol Model 220:879–887
    • (2009) Ecol Model , vol.220 , pp. 879-887
    • Anctila, F.1    Filion, M.2    Tournebizeb, J.3
  • 4
    • 84939431361 scopus 로고    scopus 로고
    • Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran
    • Chitsazan M, Rahmani R, Neyamadpour A (2013) Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran. JGeope 3(1):35–46
    • (2013) JGeope , vol.3 , Issue.1 , pp. 35-46
    • Chitsazan, M.1    Rahmani, R.2    Neyamadpour, A.3
  • 5
    • 84877286909 scopus 로고    scopus 로고
    • Application of artificial neural network in environmental water quality assessment
    • Chu HB, Lu WX, Zhang L (2013) Application of artificial neural network in environmental water quality assessment. J Agric Sci Technol 15:343–356
    • (2013) J Agric Sci Technol , vol.15 , pp. 343-356
    • Chu, H.B.1    Lu, W.X.2    Zhang, L.3
  • 6
    • 77955353319 scopus 로고    scopus 로고
    • The use of a neural network technique for the prediction of water quality parameters of Axios River in Northern Greece
    • Diamantopoulou MJ, Antonopoulos VZ, Papamichail DM (2005) The use of a neural network technique for the prediction of water quality parameters of Axios River in Northern Greece. Eur Water 11(12):55–62
    • (2005) Eur Water , vol.11 , Issue.12 , pp. 55-62
    • Diamantopoulou, M.J.1    Antonopoulos, V.Z.2    Papamichail, D.M.3
  • 7
    • 37349054250 scopus 로고    scopus 로고
    • Quantifying variability within water samples: the need for adequate subsampling
    • COI: 1:CAS:528:DC%2BD2sXhsVGlurrN
    • Donohue I, Irvine K (2008) Quantifying variability within water samples: the need for adequate subsampling. Water Res 42:476–482
    • (2008) Water Res , vol.42 , pp. 476-482
    • Donohue, I.1    Irvine, K.2
  • 8
    • 33750598352 scopus 로고    scopus 로고
    • Performance comparison of neural network training algorithms in modeling of bimodal drug delivery
    • COI: 1:CAS:528:DC%2BD28XhtFKhsLfI
    • Ghaffari A, Abdollahi H, Khoshayand MR, Bozchalooi IS, Dadgar A, Rafiee-Tehrani M (2006) Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int J Pharm 327:126–138
    • (2006) Int J Pharm , vol.327 , pp. 126-138
    • Ghaffari, A.1    Abdollahi, H.2    Khoshayand, M.R.3    Bozchalooi, I.S.4    Dadgar, A.5    Rafiee-Tehrani, M.6
  • 10
    • 84954551354 scopus 로고    scopus 로고
    • The return on investment (ROI) of data modeling. CA, Erwin
    • Haughey I (2010) The return on investment (ROI) of data modeling. CA, Erwin, March, pp 1–18
    • (2010) March , pp. 1-18
    • Haughey, I.1
  • 11
    • 42449161613 scopus 로고    scopus 로고
    • Prediction of municipal solid waste generation by use of artificial neural network: a case study of Mashhad
    • Jalili Ghazi Zade M, Noori R (2008) Prediction of municipal solid waste generation by use of artificial neural network: a case study of Mashhad. Environ Res 2(1):13–22
    • (2008) Environ Res , vol.2 , Issue.1 , pp. 13-22
    • Jalili Ghazi Zade, M.1    Noori, R.2
  • 12
    • 53549097342 scopus 로고    scopus 로고
    • Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas
    • Kim M, Gilley JE (2008) Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput Electron Agric 64:268–275
    • (2008) Comput Electron Agric , vol.64 , pp. 268-275
    • Kim, M.1    Gilley, J.E.2
  • 14
    • 0344066275 scopus 로고    scopus 로고
    • Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of black foot disease in Taiwan
    • COI: 1:CAS:528:DC%2BD3sXovFGjsb4%3D
    • Kuo YM, Liu CW, Lin KH (2004) Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of black foot disease in Taiwan. Water Res 38:148–158
    • (2004) Water Res , vol.38 , pp. 148-158
    • Kuo, Y.M.1    Liu, C.W.2    Lin, K.H.3
  • 15
    • 79951800801 scopus 로고    scopus 로고
    • A model to evaluate do of river based on artificial neural network and style book
    • Lihua C, Shengquan M, Li LI (2008) A model to evaluate do of river based on artificial neural network and style book. J Hainan Normal Univ Nat Sci 21(4):372–376
    • (2008) J Hainan Normal Univ Nat Sci , vol.21 , Issue.4 , pp. 372-376
    • Lihua, C.1    Shengquan, M.2    Li, L.I.3
  • 16
    • 77955088696 scopus 로고    scopus 로고
    • An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems
    • McKnighta S, Fundera SG, Rasmussenb JJ, Finkelc M, Binninga PJ, Bjerga PL (2010) An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems. Ecol Eng 36:1126–1137
    • (2010) Ecol Eng , vol.36 , pp. 1126-1137
    • McKnighta, S.1    Fundera, S.G.2    Rasmussenb, J.J.3    Finkelc, M.4    Binninga, P.J.5    Bjerga, P.L.6
  • 18
    • 84930220764 scopus 로고    scopus 로고
    • Predicting groundwater level surrounding Tabriz city
    • Thesis: Tabriz University
    • Nadiri A (2007) Predicting groundwater level surrounding Tabriz city. Msd. Thesis, Tabriz University
    • (2007) Msd
    • Nadiri, A.1
  • 19
    • 84855520786 scopus 로고    scopus 로고
    • Forecasting extreme PM10 concentrations using artificial neural networks
    • Nejadkoorki F, Baroutian S (2011) Forecasting extreme PM10 concentrations using artificial neural networks. J Environ Res 6(1):277–284
    • (2011) J Environ Res , vol.6 , Issue.1 , pp. 277-284
    • Nejadkoorki, F.1    Baroutian, S.2
  • 20
    • 77953122265 scopus 로고    scopus 로고
    • Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model
    • Panda Rabindra K, Pramanik N, Bala B (2010) Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Comput Geosci 36:735–745
    • (2010) Comput Geosci , vol.36 , pp. 735-745
    • Panda Rabindra, K.1    Pramanik, N.2    Bala, B.3
  • 21
    • 84954487255 scopus 로고    scopus 로고
    • Water quality prediction in distribution system using Cascade feed forward neural network. Int J Adv Technol Civil Eng
    • Patki VK, Shirihari S, Manu B (2013) Water quality prediction in distribution system using Cascade feed forward neural network. Int J Adv Technol Civil Eng, ISSN: 2231–5721, 2(1):84–91
    • (2013) ISSN: 2231–5721 , vol.2 , Issue.1 , pp. 84-91
    • Patki, V.K.1    Shirihari, S.2    Manu, B.3
  • 22
    • 84879501090 scopus 로고    scopus 로고
    • Hydro-chemical analysis of the ground water of the basaltic catchments: upper bhatsai region, Maharashtra
    • COI: 1:CAS:528:DC%2BC3MXmt1ejsLw%3D
    • Pradhan B, Pirasteh S (2011) Hydro-chemical analysis of the ground water of the basaltic catchments: upper bhatsai region, Maharashtra. Open Hydrol J 5:51–57
    • (2011) Open Hydrol J , vol.5 , pp. 51-57
    • Pradhan, B.1    Pirasteh, S.2
  • 23
    • 84939458024 scopus 로고    scopus 로고
    • Water turbidity modelling during water treatment processes using artificial neural networks
    • Rak A (2013) Water turbidity modelling during water treatment processes using artificial neural networks. Int J Water Sci 2(3):1–10
    • (2013) Int J Water Sci , vol.2 , Issue.3 , pp. 1-10
    • Rak, A.1
  • 24
    • 84939452670 scopus 로고    scopus 로고
    • Rapid field test for nitrate and ammonia in reclaimed water
    • Rich D, Washo BD, Paladini A (2006) Rapid field test for nitrate and ammonia in reclaimed water. Everglades Res Educ Center 2:2006
    • (2006) Everglades Res Educ Center , vol.2 , pp. 2006
    • Rich, D.1    Washo, B.D.2    Paladini, A.3
  • 28
    • 0035654279 scopus 로고    scopus 로고
    • Feed-forward neural network construction using cross validation
    • COI: 1:STN:280:DC%2BD3Mnltleitw%3D%3D
    • Setiono R (2001) Feed-forward neural network construction using cross validation. Neural Comput 13(12):2865–2877
    • (2001) Neural Comput , vol.13 , Issue.12 , pp. 2865-2877
    • Setiono, R.1
  • 29
    • 78649630554 scopus 로고    scopus 로고
    • Cross-property connections between overall electric conductivity and fluid permeability of a random porous media with conducting skeleton
    • Sevostianov I, Shrestha M (2010) Cross-property connections between overall electric conductivity and fluid permeability of a random porous media with conducting skeleton. Int J Eng Sci 48:1702–1708
    • (2010) Int J Eng Sci , vol.48 , pp. 1702-1708
    • Sevostianov, I.1    Shrestha, M.2
  • 31
    • 0342871690 scopus 로고    scopus 로고
    • Introduction to multi-layer feed-forward neural networks
    • COI: 1:CAS:528:DyaK2sXnsVOgu78%3D
    • Svozil D, KvasniEka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemometr Intell Lab Syst 39:43–62
    • (1997) Chemometr Intell Lab Syst , vol.39 , pp. 43-62
    • Svozil, D.1    KvasniEka, V.2    Pospichal, J.3
  • 32
    • 84939449046 scopus 로고    scopus 로고
    • Total Alkalinity
    • United States Environment Protection Agency (2013) Total Alkalinity. Retrieved 6 Mar 2013
    • (2013) Retrieved 6 Mar , pp. 2013
    • Agency, U.S.E.P.1
  • 33
    • 48949106077 scopus 로고    scopus 로고
    • Estuarine surface water allocation: a case study on the interactive role of science in support of management
    • Varnell LM, Evans DA, Bilkovic DM, Olney JE (2008) Estuarine surface water allocation: a case study on the interactive role of science in support of management. Environ Sci Policy 11:602–612
    • (2008) Environ Sci Policy , vol.11 , pp. 602-612
    • Varnell, L.M.1    Evans, D.A.2    Bilkovic, D.M.3    Olney, J.E.4
  • 34
    • 34447325995 scopus 로고    scopus 로고
    • Alkalinity and hardness in production ponds
    • Wurts WA (2002) Alkalinity and hardness in production ponds. World Aquac 33:16–17
    • (2002) World Aquac , vol.33 , pp. 16-17
    • Wurts, W.A.1
  • 35
    • 79951781387 scopus 로고    scopus 로고
    • Water simulation method based on BPNN response and analytic geometry
    • Zhang Z, Wang X, Ou Y (2010) Water simulation method based on BPNN response and analytic geometry. Proc Environ Sci 2:446–453
    • (2010) Proc Environ Sci , vol.2 , pp. 446-453
    • Zhang, Z.1    Wang, X.2    Ou, Y.3


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