Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements
Using Artificial Neural Networks for the Estimation of Subsurface Tidal Currents from High-Frequency Radar Surface Current Measurements
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DOI：
10.3390/rs13193896
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发表时间：
2021-09
期刊：
影响因子：
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通讯作者：
Max C. Bradbury;D. Conley
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文献类型：
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作者：
Max C. Bradbury;D. Conley
广泛记录水柱各层的流速是潮汐资源评估不可或缺的要求，对于准确确定整个水柱的可用能量以及估计任何特定装置可能捕获的能量都是完全必要的。传统的最小二乘法潮汐预报需要从长时间的声学多普勒海流剖面仪（ADCP）测量中计算大量的谐波参数，而长时间的原位ADCP具有测量真实的海流的优点，但后勤昂贵。这项研究的目的是展示如何克服这些问题，使用神经网络来预测整个水柱的流速，使用高频雷达测量的表面电流。各种结构的神经网络进行了训练，目的是找到网络，可以最好地模拟看不见的地下电流速度相比，ADCP数据。这项研究表明，由贝叶斯正则化算法训练的递归神经网络产生的流速与测量值高度相关：r2（0.98），平均绝对误差（0.05 ms-1）和Nash-Sutcliffe效率（0.98）。该方法证明了其高预测能力，仅使用2周的训练数据来预测未来6个月的地下电流，同时可以获得恒定的表面电流输入。由此产生的电流预测可用于计算流功率，平均误差仅为0.4%。该方法被证明是准确的谐波分析，同时需要相对较少的输入数据和优于谐波识别非天体的影响，但是，该模型仍然是网站特定的。
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.