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Open AccessArticle

Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks

by Lei Ren 1,2,3, Zhan Hu 2,3,4,* and Michael Hartnett 5
1
School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 510006, China
2
State-province Joint Engineering Laboratory of Estuarine Hydraulic Technology, Sun Yat-sen University, Guangzhou 510275, China
3
Guangdong Province Engineering Research Center for Coasts, Islands and Reefs, Sun Yat-sen University, Guangzhou 510275, China
4
School of Marine Science, Sun Yat-sen University, Guangzhou 510006, China
5
College of Engineering and Informatics, National University of Ireland Galway, Galway H91 TK33, Ireland
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 850; https://doi.org/10.3390/rs10060850
Received: 10 April 2018 / Revised: 18 May 2018 / Accepted: 24 May 2018 / Published: 31 May 2018
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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 approach—Artificial 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. View Full-Text
Keywords: surface currents; High Frequency; radars; Artificial Neural Networks; forecasts; soft computing; CODAR surface currents; High Frequency; radars; Artificial Neural Networks; forecasts; soft computing; CODAR
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MDPI and ACS Style

Ren, L.; Hu, Z.; Hartnett, M. Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks. Remote Sens. 2018, 10, 850.

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