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Using artificial neural network for reservoir eutrophication prediction

Author

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  • Kuo, Jan-Tai
  • Hsieh, Ming-Han
  • Lung, Wu-Seng
  • She, Nian

Abstract

Reservoirs provide approximately 70% of water supply for domestic and industrial use in Taiwan. The water quality of reservoirs is now one of the key factors in the operation and water quality management of reservoirs. Transient weather patterns result in highly variable magnitudes of precipitation and thereby sharp fluctuations in the surface elevation of the reservoirs. In addition, excessive watershed development in the past two decades has contributed to continuing increase in nutrient loads to the reservoirs. The difficulty in quantifying watershed nutrient loads and uncentainties in kinetic mechanism in the water column present a technical challenge to the mass balance based modeling of reservoir eutrophication. This study offers an alternative approach to quantifying the cause-and-effect relationship in reservoir eutrophication with a data-driven method, i.e., capturing non-linear relationships among the water quality variables in the reservoir. A commonly used back-propagation neural network was used to relate the key factors that influence a number of water quality indicators such as dissolved oxygen (DO), total phosphorus (TP), chlorophyll-a (Chl-a), and secchi disk depth (SD) in a reservoir in central Taiwan. Study results show that the neural network is able to predict these indicators with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ecomod:v:200:y:2007:i:1:p:171-177
    DOI: 10.1016/j.ecolmodel.2006.06.018
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    Citations

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    Cited by:

    1. Ekaterini Hadjisolomou & Konstantinos Stefanidis & George Papatheodorou & Evanthia Papastergiadou, 2016. "Assessing the Contribution of the Environmental Parameters to Eutrophication with the Use of the “PaD” and “PaD2” Methods in a Hypereutrophic Lake," IJERPH, MDPI, vol. 13(8), pages 1-14, July.
    2. Patricia Jimeno-Sáez & Javier Senent-Aparicio & José M. Cecilia & Julio Pérez-Sánchez, 2020. "Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)," IJERPH, MDPI, vol. 17(4), pages 1-14, February.
    3. 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.
    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. 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.
    6. Zhang, WenJun & Zhang, XiYan, 2008. "Neural network modeling of survival dynamics of holometabolous insects: A case study," Ecological Modelling, Elsevier, vol. 211(3), pages 433-443.
    7. Xu, Yanhong & Peng, Hong & Yang, Yinqun & Zhang, Wanshun & Wang, Shuangling, 2014. "A cumulative eutrophication risk evaluation method based on a bioaccumulation model," Ecological Modelling, Elsevier, vol. 289(C), pages 77-85.
    8. J. Yazdi & A . Moridi, 2017. "Interactive Reservoir-Watershed Modeling Framework for Integrated Water Quality Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(7), pages 2105-2125, May.
    9. Saif Said & Shadab Ali Khan, 2021. "Remote sensing-based water quality index estimation using data-driven approaches: a case study of the Kali River in Uttar Pradesh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18252-18277, December.

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