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Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

Author

Listed:
  • C. Iglesias
  • J. Martínez Torres
  • P. García Nieto
  • J. Alonso Fernández
  • C. Díaz Muñiz
  • J. Piñeiro
  • J. Taboada

Abstract

Chemical and physical-chemical parameters define water quality and are involved in water body type and habitat determination. They support a biological community of a certain ecological status. Water quality controls involve a large number of measurements of variables and observations according to the European Water Framework Directive (Directive 2000 /60/EC). In some cases, such as areas with especially critical uses or points in which potential pollution episodes are expected, the automatic monitoring is recommended. However, the chemical and physical-chemical measurements are costly and time consuming. Turbidity is shown as a key variable for the water quality control and it is also an integrative parameter. For this reason, the aim of this work is focused on this main parameter through the study of the influence of several water quality parameters on it. The artificial neural networks (ANNs) have been used in a wide range of biological problems with promising results. Bearing this in mind, turbidity values have been predicted here by using artificial neural networks (ANNs) from the remaining measured water quality parameters with success taking into account the synergistic interactions between the input variables in the Nalón river basin (Northern Spain). Finally, the main conclusions of this study are exposed. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • C. Iglesias & J. Martínez Torres & P. García Nieto & J. Alonso Fernández & C. Díaz Muñiz & J. Piñeiro & J. Taboada, 2014. "Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 319-331, January.
    • Handle: RePEc:spr:waterr:v:28:y:2014:i:2:p:319-331
    DOI: 10.1007/s11269-013-0487-9
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    2. Amanda L. Mather & Richard L. Johnson, 2016. "Forecasting Turbidity during Streamflow Events for Two Mid-Atlantic U.S. Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4899-4912, October.
    3. 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.
    4. Onur Genç & Özgür Kişi & Mehmet Ardıçlıoğlu, 2014. "Determination of Mean Velocity and Discharge in Natural Streams Using Neuro-Fuzzy and Neural Network Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(9), pages 2387-2400, July.
    5. 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.
    6. Rebecca Page & Peter Huggenberger & Gunnar Lischeid, 2015. "Multivariate Analysis of Groundwater-Quality Time-Series Using Self-organizing Maps and Sammon’s Mapping," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(11), pages 3957-3970, September.
    7. Jae Chung Park & Myoung-Jin Um & Young-Il Song & Hyun-Dong Hwang & Mun Mo Kim & Daeryong Park, 2017. "Modeling of Turbidity Variation in Two Reservoirs Connected by a Water Transfer Tunnel in South Korea," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    8. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
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