Combining deterministic modelling with artificial neural networks for suspended sediment estimates

• The presented manuscript presents a novel approach to the applied ocean and coastal engineering problem of sediment concentration estimates.
• The approach has been developed on the basis of numerical modelling and with application of artificial neural networks.
• It has been demonstrated that the proposed methodology can be generalised onto near-by locations.
• Further generalisation must be tested before applying.

Estimates of suspended sediment concentrations and transport are an important part of any marine environment assessment study because these factors have a direct impact on the life cycle and survival of marine ecosystems. This paper proposes to implement a combined methodology to tackle these estimates. The first component of the methodology comprised two numerical current and wave models, while the second component was based on the artificial intelligence technique of neural networks (ANNs) used to reproduce values of sediment concentrations observed at two sites. The ANNs were fed with modelled currents and waves and trained to produce area-specific concentration estimates. The trained ANNs were then applied to predict sediment concentrations over an independent period of observations. The use of a data set that merged together observations from both the mentioned sites provided the best ANN testing results in terms of both the normalised root mean square error (0.13) and the mean relative error (0.02).

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