Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements

Abstract

An extensive record of current velocities at all levels in the water column is an indispensable requirement for a tidal resource assessment and is fully necessary for accurate determination of available energy throughout the water column as well as estimating likely energy capture for any particular device. Traditional tidal prediction using the least squares method requires a large number of harmonic parameters calculated from lengthy acoustic Doppler current profiler (ADCP) measurements, while long-term in situ ADCPs have the advantage of measuring the real current but are logistically expensive. This study aims to show how these issues can be overcome with the use of a neural network to predict current velocities throughout the water column, using surface currents measured by a high-frequency radar. Various structured neural networks were trained with the aim of finding the network which could best simulate unseen subsurface current velocities, compared to ADCP data. This study shows that a recurrent neural network, trained by the Bayesian regularisation algorithm, produces current velocities highly correlated with measured values: r2 (0.98), mean absolute error (0.05 ms−1), and the Nash–Sutcliffe efficiency (0.98). The method demonstrates its high prediction ability using only 2 weeks of training data to predict subsurface currents up to 6 months in the future, whilst a constant surface current input is available. The resulting current predictions can be used to calculate flow power, with only a 0.4% mean error. The method is shown to be as accurate as harmonic analysis whilst requiring comparatively few input data and outperforms harmonics by identifying non-celestial influences; however, the model remains site specific.

Analysis and Prediction of Tidal Measurement Data from Temporary Stations using the Least Squares Method

“…Meanwhile, multiple statistical tests were required due to the possibility of single tests, such as the coefficient of correlation, having consistency errors. Previous research also applied quantitative methods and statistical parameters to evaluate the performance of models in forecasting wave and tidal patterns, such as the works of Ris et al [64], Ardhuin et al [65], Akpınar et al [66], Mentaschi et al [67], Bryant et al [68], Ding et al [69], Yang et al [70], Bradbury and Conley [71], and Zhang et al [72]. The statistical parameters selected to measure the quality of results from tidal prediction analysis were biased (b), Root Mean Square Error (RMSE), coefficient of correlation (R), and symmetric slope (SR).…”

Novel Neuron-like Procedure of Weak Signal Detection against the Non-Stationary Noise Background with Application to Underwater Sound

“…Neural networks are widely employed to calculate various models and algorithms for the underwater remote sensing of objects and bottom layers (see, e.g., [25][26][27][28]). Despite this, the use of the mismatch function, built on a neural basis for detecting and evaluating the parameters of received signals against various background noises, in our opinion, was proposed for the first time in [17].…”

Synchronous Assimilation of Tidal Current-Related Data Obtained Using Coastal Acoustic Tomography and High-Frequency Radar in the Xiangshan Bay, China

“…However, unlike CAT, HFR observes only surface tidal currents. Although, artificial neural networks have been used to estimate the vertical structures of tidal currents from HFR data [32], they still rely on ADCPs, and a few sites of ADCP data cannot represent the vertical features of an entire region.…”