Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity
Abstract
:1. Introduction
2. Study Area
- A tidal current velocity prediction considering the effect of the multi-layer current velocity prediction method is proposed, which enables the effect of turbulence flow to be reduced in prediction accuracy;
- The LSTM algorithm method is applied in tidal current prediction; and
- Comparative investigations of machine learning based approaches on tidal current prediction are given.
3. Methodology
3.1. Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity
3.2. Harmonic Analysis Method
3.3. Machine Learning Based Prediction Approaches
3.3.1. Long-Short Term Memory
3.3.2. Back-Propagation Artificial Neural Network
3.3.3. Elman Regression Network
4. Results
4.1. Case Study A (20 m under Sea Surface)
4.1.1. Single Input Tidal Current Prediction Method
4.1.2. Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity Method
4.2. Case Study B (30 m under Sea Surface)
4.2.1. Single Input Tidal Current Prediction Method
4.2.2. Tidal Current Velocity Prediction Considering Effect of the Multi-Layer Current Velocity Method
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Measurement Time | UTC+8 2018/2/13 12:00–2018/4/11 12:40 |
Measurement location | UTC+8 2018/2/13 12:00–2018/4/11 12:40 (Five minutes interval) |
ADCP model | Nortek AWAC 600 kHz |
Sampling frequency | 1 Hz (Continuous measurement for 30 s every 5 min) |
Ping | Continuous measurement for 30 s every 5 min |
Pulse frequency | 600 kHz |
Unit height (bin) | 1 m |
Number of units (bin) | 40 |
Method | RMSE | MAE | |
---|---|---|---|
UTide | 0.214 | 0.165 | |
Single input prediction model | LSTM | 0.142 | 0.113 |
BPANN | 0.637 | 0.512 | |
ELMAN | 0.696 | 0.584 | |
Multiple input prediction model | LSTM | 0.125 | 0.099 |
BPANN | 0.635 | 0.510 | |
ELMAN | 0.671 | 0549 |
Method | RMSE | MAE | |
---|---|---|---|
UTide | 0.177 | 0.138 | |
Single input prediction model | LSTM | 0.145 | 0.115 |
BPANN | 0.615 | 0.505 | |
ELMAN | 0.604 | 0.497 | |
Multiple input prediction model | LSTM | 0.143 | 0.109 |
BPANN | 0.6 | 0.486 | |
ELMAN | 0.603 | 0.496 |
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Feng, B.; Qian, P.; Si, Y.; Liu, X.; Yang, H.; Wen, H.; Zhang, D. Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity. Energies 2020, 13, 6417. https://doi.org/10.3390/en13236417
Feng B, Qian P, Si Y, Liu X, Yang H, Wen H, Zhang D. Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity. Energies. 2020; 13(23):6417. https://doi.org/10.3390/en13236417
Chicago/Turabian StyleFeng, Bo, Peng Qian, Yulin Si, Xiaodong Liu, Haixiao Yang, Huisheng Wen, and Dahai Zhang. 2020. "Comparative Investigations of Tidal Current Velocity Prediction Considering Effect of Multi-Layer Current Velocity" Energies 13, no. 23: 6417. https://doi.org/10.3390/en13236417