Estimate of Suspended Sediment Concentration from Monitored Data of Turbidity and Water Level Using Artificial Neural Networks

Vanessa Sari (), Nilza Maria Reis Castro and Olavo Correa Pedrollo
Additional contact information
Vanessa Sari: Federal University of Rio Grande do Sul
Nilza Maria Reis Castro: Federal University of Rio Grande do Sul
Olavo Correa Pedrollo: Federal University of Rio Grande do Sul

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 15, No 12, 4909-4923

Abstract: Abstract Artificial neural networks (ANNs) are promising alternatives for the estimation of suspended sediment concentration (SSC), but they are dependent on the availability data. This study investigates the use of an ANN model for forecasting SSC using turbidity and water level. It is used an original method, idealized to investigate the minimum complexity of the ANN that does not present, in relation to more complex networks, loss of efficiency when applied to other samples, and to perform its training avoiding the overfitting even when data availability is insufficient to use the cross-validation technique. The use of a validation procedure by resampling, the control of overfitting through a previously researched condition of training completion, as well as training repetitions to provide robustness are important aspects of the method. Turbidity and water level data, related to 59 SSC values, collected between June 2013 and October 2015, were used. The development of the proposed ANN was preceded by the training of an ANN, without the use of the new resources, which clearly showed the overfitting occurrence when resources were not used to avoid it, with Nash-Sutcliffe efficiency (NS) equals to 0.995 in the training and NS = 0.788 in the verification. The proposed method generated efficient models (NS = 0.953 for verification), with well distributed errors and with great capacity of generalization for future applications. The final obtained model enabled the SSC calculation, from water level and turbidity data, even when few samples were available for the training and verification procedures.

Keywords: Water level; Turbidity; Optical sensors; Suspended sediment concentration; Artificial neural networks (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-017-1785-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:31:y:2017:i:15:d:10.1007_s11269-017-1785-4

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-017-1785-4

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().