Estimation of Coastal Currents Using a Soft Computing Method: A Case Study in Galway Bay, Ireland

Abstract

In order to obtain forward states of coastal currents, numerical models are a commonly used approach. However, the accurate definition of initial conditions, boundary conditions and other model parameters are challenging. In this paper, a novel application of a soft computing approach, random forests (RF), was adopted to estimate surface currents for three analysis points in Galway Bay, Ireland. Outputs from a numerical model and observations from a high frequency radar system were used as inputs to develop soft computing models. The input variable structure of soft computing models was examined in detail through sensitivity experiments. High correlation of surface currents between predictions from RF models and radar data indicated that the RF algorithm is a most promising means of generating satisfactory surface currents over a long prediction period. Furthermore, training dataset lengths were examined to investigate influences on prediction accuracy. The largest improvement for zonal and meridional surface velocity components over a 59-h forecasting period was 14% and 37% of root mean square error (RMSE) values separately. Results indicate that the combination of RF models with a numerical model can significantly improve forecasting accuracy for surface currents, especially for the meridional surface velocity component.

Radar Technology for Coastal Areas and Open Sea Monitoring

“…Aside from the specific results focusing on the dynamics of the GoN, the present study also provides wider-breath elements in relation to the potential use of HF radars as a long-term monitoring platform for wave fields. ADCPs have a well-documented track of records in surface wave measurements [83], whereas HF radars have a proven ability to measure surface currents accurately even over long temporal scales (e.g., [84,85]). In contrast, only a few works have used HF radar data to characterize the wave field over periods as long as almost two years [30][31][32][33][34].…”

Editorial for Special Issue “Radar Technology for Coastal Areas and Open Sea Monitoring”

“…A general classification of the contributions included in the present SI may be provided on the basis of the adopted remote sensing monitoring systems. Specifically, some of the presented studies deal with analyses of data acquired from radar systems installed in coastal areas, such as HF radar [7][8][9][10][11][12][13][14] and X-band nautical radar systems [15,16], while others were mounted on satellite platforms, such as Synthetic Aperture Radar (SAR) [17,18] and Radar Altimeters (RAs) [19]. An overview of the contributions published is provided in the following section, showing the wide range of applications and methodologies covered in the SI.…”

“…The authors show seasonal patterns in surface structures, but also reversal episodes, unveiling relationships with atmospheric and oceanic forcings, as well as with tides. The authors in [8] provide a new application for soft-computing techniques (random forest, RF) to forecast surface current fields in Galway Bay (Ireland). In this study, HF radar-derived fields, coupled with numerical outputs, are used as inputs for the RF model.…”

A new Lagrangian-based short-term prediction methodology for high-frequency (HF) radar currents

“…Several studies have compared in situ current measurements with HFR observations (e.g., Schott et al, 1985;Hammond et al, 1987;Rosenfeld, 1996, Emery et al, 2004;Paduan et al, 2006;Ohlmann et al, 2007;Liu et al, 2014;Solabarrieta et al, 2014;Bellomo et al, 2015;Lana et al, 2016;Hernández-Carrasco et al, 2018b) and have repeatedly demonstrated the potential of this technology. Presently, more than 250 HFR antennas are installed and active worldwide (Roarty et al, 2019; http://global-hfradar.org/, last access: 26 May 2021).…”