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Article

High-Precision Combined Tidal Forecasting Model

Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Navigation College, Dalian Maritime University, Dalian 116026, China
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Algorithms 2019, 12(3), 65; https://doi.org/10.3390/a12030065
Received: 9 February 2019 / Revised: 18 March 2019 / Accepted: 25 March 2019 / Published: 26 March 2019
(This article belongs to the Special Issue Big Data Analytics, Algorithms and Programming)
To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide can be considered as a superposition of the astronomical tide level and the non-astronomical tidal level, which are affected by the tide-generating force and environmental factors, respectively. The tidal data are de-noised via wavelet analysis, and the astronomical tide level is subsequently calculated via harmonic analysis. The residual sequence generated via harmonic analysis is used as the sample dataset of the non-astronomical tidal level, and the tidal height of the system is calculated by the ARIMA-SVR model. Finally, the tidal values are predicted by linearly summing the calculated results of both systems. The simulation results were validated against the measured tidal data at the tidal station of Bay Waveland Yacht Club, USA. By considering the residual non-astronomical tide level effects (which are ignored in traditional harmonic analysis), the combined model improves the accuracy of tidal prediction. Moreover, the combined model is feasible and efficient. View Full-Text
Keywords: tidal level prediction; combined model; harmonic analysis method; Support Vector Regression (SVR); Autoregressive Integrated Moving Average Model (ARIMA) tidal level prediction; combined model; harmonic analysis method; Support Vector Regression (SVR); Autoregressive Integrated Moving Average Model (ARIMA)
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MDPI and ACS Style

Liu, J.; Shi, G.; Zhu, K. High-Precision Combined Tidal Forecasting Model. Algorithms 2019, 12, 65. https://doi.org/10.3390/a12030065

AMA Style

Liu J, Shi G, Zhu K. High-Precision Combined Tidal Forecasting Model. Algorithms. 2019; 12(3):65. https://doi.org/10.3390/a12030065

Chicago/Turabian Style

Liu, Jiao, Guoyou Shi, and Kaige Zhu. 2019. "High-Precision Combined Tidal Forecasting Model" Algorithms 12, no. 3: 65. https://doi.org/10.3390/a12030065

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