Publication

Short-term forecasting of coastal surface currents using high frequency radar data and artificial neural networks

Ren, Lei
; 
Hu, Zhan
; 
Hartnett, Michael
Repository DOI
Publication Date
2018-05-31
Keywords
surface currents, high frequency, radars, artificial neural networks, forecasts, soft computing, codar, fuzzy inference system, self-organizing maps, mediterranean sea, wave predictions, galway bay, assimilation, model, wind, performance, experience
Type
Article
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Citation
Ren, Lei; Hu, Zhan; Hartnett, Michael (2018). Short-term forecasting of coastal surface currents using high frequency radar data and artificial neural networks. Remote Sensing 10 (6),
Abstract
Accurate and timely information of surface currents is crucial for various operations such as search and rescue, marine renewable energy extraction and oil spill treatment. Conventional approaches to study coastal surface currents are numerical models and observation platforms such as radars and satellites. However, both have limits. To efficiently obtain high accuracy short-term forecasting states of oceanic parameters of interest, a robust soft computing approachArtificial Neural Networks (ANN)was applied to predict surface currents in a tide- and wind-dominated coastal area. Hourly observed surface currents from a Coastal Ocean Dynamic Application Radar (CODAR) system, and tide and wind data from forecasting models were used to establish ANN models for Galway Bay area. One of the fastest algorithms, resilient back propagation, was used to adapt all weights and biases. This study focused on investigating the sensitivity of an ANN model to a series of different input datasets. Results indicate that correlation between ANN forecasts and observation was greater than 0.9 for both surface velocity components with one-hour lead time. Strong correlation (0.75) was obtained between predicted results and radar data for both surface velocity components with three-hour lead time at best. However, forecasting accuracy deteriorated rapidly with longer lead time. By comparison with previous data assimilation models, in this research, best performance was achieved from ANN model's peak times of the tidally dominant surface velocity component. The forecasts presented in this research show clear improvements over previous attempts at short-term forecasting of wind- and tide-dominated currents using ANN.
Funder
Publisher
MDPI AG
Publisher DOI
10.3390/rs10060850
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland
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Externally hosted open access publications with University of Galway authors (2)