Combining deterministic modelling with artificial neural networks for suspended sediment estimates

Oleg Makarynskyy, Dina Makarynska, Matthew Rayson, Scott Langtry

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Abstract 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).

Original languageEnglish
Article number3035
Pages (from-to)247-256
Number of pages10
JournalApplied Soft Computing Journal
Volume35
DOIs
Publication statusPublished - 15 Jul 2015
Externally publishedYes

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