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Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia

Author

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  • Ranković, Vesna
  • Radulović, Jasna
  • Radojević, Ivana
  • Ostojić, Aleksandar
  • Čomić, Ljiljana

Abstract

The objective of this study is to develop a feedforward neural network (FNN) model to predict the dissolved oxygen in the Gruža Reservoir, Serbia. The neural network model was developed using experimental data which are collected during a three years. The input variables of the neural network are: water pH, water temperature, chloride, total phosphate, nitrites, nitrates, ammonia, iron, manganese and electrical conductivity. Sensitivity analysis is used to determine the influence of input variables on the dependent variable. The most effective inputs are determined as pH and temperature, while nitrates, chloride and total phosphate are found to be least effective parameters. The Levenberg–Marquardt algorithm is used to train the FNN. The optimal FNN architecture was determined. The FNN architecture having 15 hidden neurons gives the best choice. Results of FNN models have been compared with the measured data on the basis of correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). Comparing the modelled values by FNN with the experimental data indicates that neural network model provides accurate results.

Suggested Citation

  • Ranković, Vesna & Radulović, Jasna & Radojević, Ivana & Ostojić, Aleksandar & Čomić, Ljiljana, 2010. "Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia," Ecological Modelling, Elsevier, vol. 221(8), pages 1239-1244.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:8:p:1239-1244
    DOI: 10.1016/j.ecolmodel.2009.12.023
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    References listed on IDEAS

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    1. Hull, Vincent & Parrella, Luisa & Falcucci, Margherita, 2008. "Modelling dissolved oxygen dynamics in coastal lagoons," Ecological Modelling, Elsevier, vol. 211(3), pages 468-480.
    2. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
    3. Kuo, Jan-Tai & Hsieh, Ming-Han & Lung, Wu-Seng & She, Nian, 2007. "Using artificial neural network for reservoir eutrophication prediction," Ecological Modelling, Elsevier, vol. 200(1), pages 171-177.
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    1. Tat Pham Van & Pham Nu Ngoc Han & Minh Phap Dao, 2017. "Modelling of Dissolved Oxygen in Thi Vai River Water Incorporating Artificial Neural Network and Multivariable Regression," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 7(1), pages 11-18, November.
    2. Rana Muhammad Adnan & Hong-Liang Dai & Reham R. Mostafa & Kulwinder Singh Parmar & Salim Heddam & Ozgur Kisi, 2022. "Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    3. Gebdang B. Ruben & Ke Zhang & Hongjun Bao & Xirong Ma, 2018. "Application and Sensitivity Analysis of Artificial Neural Network for Prediction of Chemical Oxygen Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 273-283, January.
    4. Areerachakul, Sirilak & Sophatsathit, Peraphon & Lursinsap, Chidchanok, 2013. "Integration of unsupervised and supervised neural networks to predict dissolved oxygen concentration in canals," Ecological Modelling, Elsevier, vol. 261, pages 1-7.
    5. Luke Durell & J. Thad Scott & Douglas Nychka & Amanda S. Hering, 2023. "Functional forecasting of dissolved oxygen in high‐frequency vertical lake profiles," Environmetrics, John Wiley & Sons, Ltd., vol. 34(4), June.

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