DOI QR코드

DOI QR Code

Modelling of dissolved oxygen (DO) in a reservoir using artificial neural networks: Amir Kabir Reservoir, Iran

  • Received : 2016.08.23
  • Accepted : 2016.10.10
  • Published : 2016.09.25

Abstract

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

Keywords

References

  1. Asadollahfardi, G. Taklify, A. and Ghanbari, A. (2012), "Application of artificial neural network to predict TDS in Talkheh Rud River", J. Irrigat. Drain. Eng., 138(4), 363-370. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000402
  2. Asadollahfardi, G. Moradinejad, S. and Asadollahfardi, R. (2013), "Sodium adsorption ratio (SAR) prediction of Chalghazi River using artificial neural network (ANN), Iran", Curr. World Sign., Syst., 2(4), 303-314.
  3. Baxter, C.W. (2001), Artificial Intelligence Systems for Water Treatment Plant, AWWA Research Foundation, American water works association, ISBN1-58321-140-3, USA.
  4. Chen, W.B. and Liu, W.C. (2014), "artificial neural network modeling of dissolved oxygen in reservoir", J. Environ. Monit. Assess., 186(2), 1203-1217. https://doi.org/10.1007/s10661-013-3450-6
  5. Cybenko, G. (1989), "Approximation by superposition of a sigmoid function Mathematics of Control, Signals, and Systems (MCSS), 2(4), 303-314. https://doi.org/10.1007/BF02551274
  6. Dogan, E. Koklu and Sengorur, B. (2007), "Estimation of biological oxygen demand using artificial neural network", International Earthquake Symposium, Kocaeli, Turkey, October.
  7. Dogan, E., Sengorur, B.B. and Koklu, R. (2009), "Modeling the biological oxygen demand of the Melan River in Turkey using an artificial neural network technique", J. Environ. Manage., 90, 1229-1235. https://doi.org/10.1016/j.jenvman.2008.06.004
  8. Dawson, C.W. and Wibly, R.L. (2001), "Hydrological modeling using artificial neural networks", Prog. Phys. Geography, 25(80), 81-108.
  9. Gardner, C.W. and Dorling, S.R. (1998), "Artificial neural network (the multilayer perceptron)-a review of application in atmospheric sciences", Atmospher. Environ., 32(14-15), 2626-2636.
  10. Han, H.L. Chen, Q-L. and Qiao, J.-F. (2011), "An efficient self-organizing RBF neural network for water quality prediction", Neural Networks, 24(7), 717-725. https://doi.org/10.1016/j.neunet.2011.04.006
  11. Hornik, K.M., Stinchocombe, M. and White, H. (1989), "Multilayer feed forward networks are universal approximators", Neural Networks, 2(5), 359-366. https://doi.org/10.1016/0893-6080(89)90020-8
  12. Hornik, K.M. (1991), "Approximation capabilities of multilayer feedforward Networks", Neural Networks, 4(2), 251-257. https://doi.org/10.1016/0893-6080(91)90009-T
  13. Hornik, K.M. (1993), "Some new results on neural network approximation", Neural Networks, 6(8), 1069-1072. https://doi.org/10.1016/S0893-6080(09)80018-X
  14. Huang, W. and Foo, S. (2002), "Neural network modeling of salinity variation in the Apalachicola River", Water Res., 36(1), 356-362 https://doi.org/10.1016/S0043-1354(01)00195-6
  15. Ilanloo, M. (2011), "A comparative study of the fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj Dam basin in Iran", Procedia Soc. Behav. Sci., 19, 668-676. https://doi.org/10.1016/j.sbspro.2011.05.184
  16. Karakaya, N., Evrendilek, F. and Gungor, K. (2011), "Modeling and validating long-term dynamics of Diel dissolved oxygen with particular reference to pH in a temperate shallow lake (Turkey)", Clean-Soil, Air, Water, 39 (11), 966-971. https://doi.org/10.1002/clen.201100051
  17. Keiner, L.E. and Yan, X. (1998), "A neural network model for Estimation Sea surface chlorophyll mapper imaginary", Remote Sens. Environ., 66(2), 153-163. https://doi.org/10.1016/S0034-4257(98)00054-6
  18. Kohohen, T. (1984), Self-organization and Associative Memory, New York: Springer-Verlag.
  19. Krause, P., Boyle, D. and Base, F. (2005), "Comparison of different efficiency criteria for hydrological model assessment", Adv. Geosci. J., 5, 89-97. https://doi.org/10.5194/adgeo-5-89-2005
  20. Kuo, Y-M., Liu, C-W. and Lin, K-H. (2004), "Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of Blackfoot disease in Taiwan", Water Res., 38(1), 148-158. https://doi.org/10.1016/j.watres.2003.09.026
  21. Leshno, M., Lin, V.Y., Pinkus, A. and Schocken, S. (1993), "Multilayer feedforward networks with a nonpolynomial activation function can approximate any function", Neural Networks, 6(6), 861-867. https://doi.org/10.1016/S0893-6080(05)80131-5
  22. Musavi-Jahromi, S.H. and Golabi, M. (2008), "Application of artificial neural network in the river water quality modeling. Karoon River, Iran", J. Appl. Sci., 8(12), 2324-2328. https://doi.org/10.3923/jas.2008.2324.2328
  23. Menhaj, M. (1998), Computational Intelligence, Fundamentals of Artificial Neural Networks, Vol.1 Amirkabir University publisher.
  24. Rene, E.R. and Saidutta, M.B. (2008), "Prediction of water quality indices by regression analysis and artificial neural networks", Int. J. Environ. Res., 2(2), 183-188,
  25. Razavi, F. (2006), "Rain prediction applying artificial neural network", M.S thesis, Amir Kabir Univ., Tehran, Iran.
  26. Rucinski, D.K., Beletsky, D., DePinto, J.V., Schwab, D.J. and Scavia, D. (2010), "A simple 1-dimensional, climate based dissolved model to central basin of Lake Erie", J. Great Lakes Res., 36(3), 465-476. https://doi.org/10.1016/j.jglr.2010.06.002
  27. Singh K.P. Basant, A. Malik, A. and Jain, G. (2009), "Artificial neural network modelling of the river water quality-A case study", Ecological Model., 220(6), 888-895. https://doi.org/10.1016/j.ecolmodel.2009.01.004
  28. Song, X.M. (1996), "Radial basis function networks for empirical modeling of chemical process", MSc thesis, University of Helsinki.
  29. Willmott, C. and Matsuura, K. (2005), "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance", Climate Res. J., 30(1), 79-82. https://doi.org/10.3354/cr030079
  30. Zealand, C.M., Burn, D.H. and Simonovic, S.P. (1999), "Short term stream flow forecasting using artificial neural networks", J. Hydrol., 214(1), 32-48. https://doi.org/10.1016/S0022-1694(98)00242-X

Cited by

  1. Prediction of UCS and STS of Kaolin clay stabilized with supplementary cementitious material using ANN and MLR vol.5, pp.2, 2016, https://doi.org/10.12989/acd.2020.5.2.195