PREDICTION OF DISSOLVED OXYGEN IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs)
Keywords:
FEED-FORWARD NEURAL NETWORK (FFNN), WATER QUALITY MODELING, DISSOLVED OXYGEN, TIGRIS RIVER
Abstract
The purpose of this study is to develop a feed-forward neural network (FFNN) model with back-propagation learning algorithm to predict the dissolved oxygen from water temperature and 5 days-biological oxygen demand in the Tigris River, Baghdad-Iraq. The Artificial Neural Networks model was implemented utilizing measured data that were gathered from laboratories of water treatment plant, Baghdad-Iraq, during the year 2008. The correlation analysis between dissolved oxygen and dependent parameters were utilized in selecting the major inputs from water quality parameters for commencing of ANN models. The performance of ANN models were tested utilizing the coefficient of correlation (R), the efficiency coefficient of Nash-Sutcliffe (NS), mean square error (MSE) and mean absolute errors (MAE). The outputs revealed that the feed-forward neural networks using back-propagation learning algorithm which was prepared by temperature and biological oxygen demand offered a relatively high correlation coefficient of 0.885, and efficiency coefficient of 0.782, meanwhile a reasonably low mean square errors of 1.133, and mean absolute errors of 0.369 values for whole array period. The results of the present study demonstrate that the artificial neural networks using FFNN model is capable to forecast the dissolved oxygen values with acceptable accuracy. This is suggesting that the artificial neural network is a useful tool for Tigris River management in Baghdad-Iraq.
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References
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Zhu, M.L., Fujita, M., (1994). Comparison between fuzzy reasoning and neural networks methods to forecast runoff discharge. Journal of Hydrology Science & Hydraulic Engineering, 12 (2), 131–141.
Abyaneh, H.Z., (2014). Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters. Journal of Environmental Health Science & Engineering, 12 (40), 1–8.
Ahmed, A.A.M. (2014). Prediction of dissolved oxygen in Surma River by biochemical oxygen demand and chemical oxygen demand using the artificial neural networks (ANNs). Journal of King Saud University – Engineering Sciences, 2(29), 151-158.
Ahmed, A.A.M., Hossain, M.I., Rahman, M.T., Chowdhury, M.A.I., (2013). Application of artificial neural network models for predicting dissolved oxygen concentration for Surma River, Bangladesh. Journal of Applied Technology in Environmental Sanitation, 3 (3), 135–140.
Areerachakul et al., (2011). Prediction of Dissolved Oxygen Using Artificial Neural Network, International Conference on Computer Communication and Management Proc .of CSIT vol.5 (2011) © (2011) IACSIT Press, Singapore.
Ay, M., Kisi, O., (2012). Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado, USA. Journal of Environmental Engineering, 138, 654-662.
Boano, F., Revelli, R., Ridolfi, L., (2006). Stochastic modeling of DO and BOD components in a stream with random inputs. Advances inWater Resources 29(9), 1341-1350.
Chapra, S., Pellettier, G., (2003). QUAL2K: A modeling framework for simulating river and stream water quality. Documentation and user’s manual. Civil and Environmental Engineering Dept., Tufts University, Medford, MA. Available from:
Csábrági et al., (2015). Forecasting of dissolved oxygen in the river danube using neural networks, Journal of hungarian agricultural engineering, N° 27/2015, 38-41.
Ehsan O., Hamid Z. A., Ali D. M., (2016). Comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River. Journal of Geoscience Frontiers. 1-11.
French, M.N., Krajewski, W.F., Cuykendall, R.R., (1992). Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137, 1–31.
G.Civelekoglu, N.O. Yigit, E.Diamadopoulos and M.Kitis, (2007). “Prediction of Bromate Formation Using Multi- Linear Regression and Artificial Neural Networks,” Journal of Ozone Science and Engineer, Taylor&Francis, vol.5, no.5: pp.353-362.
Garcia, A., Revilla, J.A., Medina, R., Alvarez, C., Juanes, J.A., (2002). A model for predicting the temporal evolution of dissolved oxygen concentration in shallow estuaries. Hydrobiology 475-476, 205-211.
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice-Hall, New Jersey, pp.842.
Hull, V., Parrella, L., Falcucci, M., (2008). Modelling dissolved oxygen dynamics in coastal lagoons. Journal of Ecological Modelling, 2, 468-480.
Kalff, J., (2002). Limnology: Inland Water Ecosystems. Prentice-Hall, Upper Saddle River, NJ.
Kisi, O., Akbari, N., Sanatipour, M., Hashemi, A., Teimourzadeh, K., Shiri, J., (2013). Modeling of dissolved oxygen in river water using artificial intelligence techniques. Journal of Environmental Informatics, 22, 92-101.
Kuo, J.T., Hsieh, M.H., Lung, W.S., She, N., (2007). Using artificial neural network for reservoir eutrophication prediction. Journal of Ecological Modeling. 200, 171–177.
Matti, L. Y.,(2014). Seasonal variations of raw water of Tigris river and effect on quality water plants. Journal of University of Duhok: Pure and Engineering Sciences 1(17), 55-71.
Nash, J.E., Sutcliffe, J.V., (1970). River flow forecasting through conceptual models, part I, a discussion of principles. Journal of Hydrology, 10 (3), 282–290.
Niroobakhsh, M.S.H., Musavi-Jahromi, S.H., Manshouri, M., Sedghi, S., (2012). Prediction of water quality parameter in Jajrood River basin: Application of multi-layer perceptron (MLP) perceptron and radial basis function networks of artificial neural networks (ANNs). African Journal of Agricultural Research, 7 (29), 4131–4139.
Nourani, V., Kisi, O., Komasi, M., (2011). Two hybrid Artificial Intelligence approaches for modeling rainfallerunoff process. Journal of Hydrology, 402, 41-59.
Rankovic, V., Radulovi, J., Radojevic, I., Ostojic, A., Comic, L., (2010). Neural network modeling of dissolved oxygen in the Gruza reservoir, Serbia. Journal of Ecological Modeling, 221, 1239–1244.
S.H.Musavi and M.Golabi,(2008). Application of Artificial Neural Networks in the River Water Quality Modeling: Karoon River,Iran. Journal 0f Applied Sciences, Asian Network for Scientific Information, 2324-2328.
Sarkar, A. and Pandey, P., (2015). River Water Quality Modelling using Artificial Neural Network Technique. Aquatic Procedia, 4, 1070 – 1077.
Shu, C., Ouarda, T.B.M.J., (2008). Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology, 349, 31-43.
Shukla, J.B., Misra, A.K., Chandra, P., (2008). Mathematical modeling and analysis of the depletion of dissolved oxygen in eutrophied water bodies affected by organic pollutants. Nonlinear Analysis: Real World Applications, 9(5), 1851-1865.
Simon Haykin,(2005). Neural Networks: A Comprehensive foundation second edition. Pearson Prentice Hall, Delhi India.
Singh, K.P., Basant, A., Malik, A., Jain, G., (2009). Artificial neural network modeling of the river water quality-a case study. Journal of Ecological Modeling, 220, 888–895.
Stefan, H.G., Fang, X., Wright, D., Eaton, J.G., McCormick, J.H., (1995). Simulation of dissolved oxygen profiles in a transparent, Dimictic Lake. Limnol. Oceanogr. 40, 105–118.
Wool, T.A., Ambrose, R.B., Martin, J.L., Comer, E.A., (2006). Water Quality Analysis Simulation Program (WASP) Version 6.0 DRAFT: User’s Manual. U.S. Environmental Protection Agency, Athens, GA. Available from:
Yi-Ming, K., Chen, W.L., Kao-Hung, L., (2003). 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.,1–8.
Ying, Z., Jun, N., Fuyi, C., Liang, G., (2007). Water quality forecast through application of BP neural network at Yuquio reservoir. Journal of Zhejiang University Science A 8, 1482–1487
YSI., (2009). The dissolved Oxygen handbook. In: We Know D.O. YSI Incorporated, p. 76.
Zhu, M.L., Fujita, M., (1994). Comparison between fuzzy reasoning and neural networks methods to forecast runoff discharge. Journal of Hydrology Science & Hydraulic Engineering, 12 (2), 131–141.
Published
2017-07-29
How to Cite
OMAR, K. A. (2017). PREDICTION OF DISSOLVED OXYGEN IN TIGRIS RIVER BY WATER TEMPERATURE AND BIOLOGICAL OXYGEN DEMAND USING ARTIFICIAL NEURAL NETWORKS (ANNs). Journal of Duhok University, 20(1), 691-700. https://doi.org/10.26682/sjuod.2017.20.1.60
Section
Pure and Engineering Sciences
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