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Articles

Application of artificial neural networks to estimating DO and salinity in San Joaquin River basin

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Pages 4888-4897 | Received 10 Mar 2014, Accepted 01 Dec 2014, Published online: 02 Jan 2015

References

  • Stockholm International Water Institute and Elsevier, The Water and Food Nexus: Trends and Development of the Research Landscape, 2012.
  • L.M. Varnell, D.A. Evans, D.M. Bilkovic, J.E. Olney, Estuarine surface water allocation: A case study on the interactive role of science in support of management, Environ. Sci. Policy 11 (2008) 602–612.10.1016/j.envsci.2008.05.003
  • I.M. Schleiter, D. Borchardt, R. Wagner, T. Dapper, K. Schmidt, H. Schmidt, H. Werner, Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks, Ecol. Modell. 120 (1999) 271–286.10.1016/S0304-3800(99)00108-8
  • I. Donohue, K. Irvine, Quantifying variability within water samples: The need for adequate subsampling, Water Res. 42 (2008) 476–482.10.1016/j.watres.2007.07.041
  • H. Scholten, A. Kassahun, J.C. Refsgaard, T. Kargas, C. Gavardinas, A.J.M. Beulens, A methodology to support multidisciplinary model-based water management, Environ. Modell. Softw. 22 (2007) 743–759.10.1016/j.envsoft.2005.12.025
  • U.S. McKnight, S.G. Funder, J.J. Rasmussen, M. Finkel, P.J. Binning, P.L. Bjerg, An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems, Ecol. Eng. 36 (2010) 1126–1137.10.1016/j.ecoleng.2010.01.004
  • T. Haughey, The Return on Investment (ROI) of Data Modeling, CA Erwin, March, 2010 1–18.
  • M.J. Diamantopoulou, V.Z. Antonopoulos, D.M. Papamichail, The use of a neural network technique for the prediction of water quality parameters of Axios River in Northern Greece, Eur. Water 11(12) (2005) 55–62.
  • C. Lihua, M. Shengquan, L. Li, A model to evaluate DO of river based on artificial neural network and style book, J. Hainan Normal Univ. (Nat. Sci.) 21(4) (2008) 372–376.
  • V.H. McNeil, M.E. Cox, Relationship between conductivity and analysed composition in a large set of natural surface-water samples, Queensland, Australia, Environ. Geol. 39(12) (2000) 1325–1333.10.1007/s002549900033
  • N. Granlund, A. Lundberg, D. Gustafsson, Laboratory study of salinity influence on the relationship between electrical conductivity and wetness of snow, in: 65th Eastern Snow Conference, Fairlee (Lake Morey), VT, USA, 2008, pp. 301–308.
  • The Clean Water Team (CWT), Guidance Compendium for Watershed Monitoring and Assessment State Water Resources Control Board FS-3.1.3.0., (EC) V2e (2004) 1–5.
  • R.S. Ayers, D.W. Westcot, Water Quality for Agriculture, FAO Irrigation and Drainage Paper 29, 1994.
  • T. Koncsos, The application of neural networks for solving complex optimization problems in modeling, Conference of Junior Researchers in Civil Engineering (2010) 97–102.
  • V.K. Patki, S. Shrihari, B. Manu, Water quality prediction in distribution system using cascade feed forward neural network, Int. J. Adv. Technol. Civ. Eng. 2(1) (2010) 84–91 ( 2231–5721).
  • S.A. Rounds, Development of a neural network model for dissolved oxygen in the Tualatin river, Oregon, in: Second Federal Interagency hydrologic modeling conference, Las Vegas, NV 1 (2002) 1–13.
  • I.G.A.C. Cordoba, Using of artificial neural network for evaluation and prediction of some drinking water quality parameters within a water distribution system, Water Management and Water Structures, Juniorstav, 2011, pp. 1–11.
  • R.A. Aziz, K.F.V. Wong, A neural-network approach to the determination of aquifer parameters, Ground Water 30(2) (1992) 164–166.10.1111/gwat.1992.30.issue-2
  • C. Paulin, Artificial neural network modeling of water table depth fluctuations, Water Resour. Res. 374 (2001) 885–896.
  • M. Chitsazan, G. Rahmani, A. Neyamadpour, Groundwater level simulation using artificial neural network: A case study from Aghili plain, urban area of Gotvand, south-west Iran, J. Geope. 3 (2013) 35–46.
  • A. Rak, Water turbidity modelling during water treatment processes using artificial neural networks, Int. J. Water Sci. 2 (2013) 1–10.10.5772/16
  • R.K. Pandan, N. Pramanik, B. Bala, Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model, Comput. Geosci. 36 (2010) 735–745.
  • H.B. Chu, W.X. Lu, L. Zhang, Application of artificial neural network in environmental water quality assessment, J. Agr. Sci. Technol. 15 (2013) 343–356.
  • J. Ghazi Zade, R. Noori, Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad, Environ. Res. 2 (2008) 13–22.
  • F. Nejadkoorki, S. Baroutian, Forecasting extreme PM10 concentrations using artificial neural networks, J. Environ. Res. 6 (2010) 277–284.
  • G. Steyl, Application of Artificial Neural Networks in the Field Of Geohydrology, University of the Free State, Bloemfontein, 2009.
  • P.D. Sreckanth, N. Geethanjali, P.D. Sreedevi, S. Ahmed, N. Ravi Kumar, P.D.K. Jayanthi, Forecasting groundwater level using artificial neural networks, J. Curr. Sci. 96(7) (2009) 933–939.
  • A. Nadiri, Predicting Groundwater Level Surrounding Tabriz City, thesis, Tabriz University, Iran, 2011.
  • C.M. Zealand, D.H. Burn, S.P. Simonovic, Short term streamflow forecasting using artificial neural networks, J. Hydrol. 214 (1999) 32–48.10.1016/S0022-1694(98)00242-X
  • M.C. Demirel, A. Venancio, E. Kahya, Flow forecast by SWAT model and ANN in Pracana basin, Portugal, Adv. Eng. Softw. 40 (2009) 467–473.10.1016/j.advengsoft.2008.08.002
  • D. Svozil, V. Kvasnicka, J. Pospichal, Introduction to multi-layer feed-forward neural networks, Chemom. Intell. Lab. Syst. 39 (1997) 43–62.10.1016/S0169-7439(97)00061-0
  • A.G. Carpenter, Neural network models for pattern recognition and associative memory, Neural Networks 2 (1989) 243–257.10.1016/0893-6080(89)90035-X
  • M.B. Menhaj, Fundamental of Neural Network, vol. 1. Industrial Amir Kabir University, Tehran, 2008.
  • A. Abraham, Artificial Neural Networks. Oklahoma State University, Stillwater, OK, 2005, pp. 901–908.
  • L. Fausett, Fundamentals of Neural Networks Architectures, Algorithms and Applications. Prentice Hall, Englewood Cliffs, NJ, 1994.
  • S. Haykin, Neural networks: A comprehensive foundation, Macmillan, New York, NY, 1994.
  • U.F. Dowla, L. Rogers, Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks, MIT Press, Cambridge, MA, 1995.
  • K. Gurney, An Introduction to Neural Network, UCL Press, London, 1999.
  • D. Patterson, Artificial Neural Networks, Prentice Hall, Singapore, 1996.
  • www.water.ca.gov\WaterDataLibraryContinuousTimeSeriesData.htm

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