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Water Resources Management

, Volume 31, Issue 15, pp 4909–4923 | Cite as

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

  • Vanessa Sari
  • Nilza Maria dos Reis Castro
  • Olavo Correa Pedrollo
Article

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 

Notes

Acknowledgements

The authors would like to thank CNPq for the first author’s doctorate scholarship and for the second author’s research production grant, and FINEP for the allocation of resources for the basin monitoring in the projects “Research Network in Monitoring and Modeling of Hydrosedimentological Processes in Rural and Urban Representative Basins of the Atlantic Forest Biome” and “Assessment of the Sediment Input in Reservoirs Aiming at Increasing Water Availability”.

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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Institute of Hydraulic ResearchFederal University of Rio Grande do SulPorto AlegreBrazil

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