Application of Kalman Filter to Short-Term Tide Level Prediction

Yen, Pei Hwa; Jan, Chyan Deng; Lee, Youe Ping; Lee, Hsiu Fang

doi:10.1061/(asce)0733-950x(1996)122:5(226)

Cited by 38 publications

(10 citation statements)

References 7 publications

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“…Mathematical statistics methods are mainly based on the mathematical law of sea level change time series, which is used to fit and extrapolate data [5]. Early mathematical statistical methods included simple linear regression [6], multivariate stepwise regression, maximum entropy spectrum analysis [7], and Kalman filtering [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Liu

Jiao

Sun

et al. 2021

Scientific Programming

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The South China Sea is China’s largest marginal sea area, and it is rich in oil and gas mineral resources; thus, estimating its sea level changes is of practical significance. Based on linear and nonlinear sea level change characteristics, this paper decomposes 1992–2019 monthly mean sea level anomaly time series in the South China Sea into trend, seasonal, and random terms. This paper compares the seasonal autoregressive integrated moving average (SARIMA) and Prophet models for estimating the trend and seasonal terms and the long short-term memory (LSTM) and radial basis function (RBF) models for estimating random terms, and the more suitable models were selected. A Prophet-LSTM combined model was developed based on the accuracy results. This paper uses the combined model to study the effect of known data length on the experimental results and determines the best prediction duration. The results show that the combined model is suitable for short-term and medium-term estimations of 12–36 months. The accuracy at 36 months is 0.962 cm, which proves that the combined model has high application value for estimating sea level changes in the South China Sea.

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Section: Introductionmentioning

confidence: 99%

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Liu

Jiao

Sun

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

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“…Doodson determined the harmonic analysis constants by least-squares fitting the observed tidal data [4]. Yen smoothed the harmonic analysis constants by passing them through a Kalman filter [5]. After hundreds of years of development, harmonic analysis continues to be widely used in tidal prediction; however, this model only considers the astronomical tidal level affected by the tide-generating forces.…”

Section: Introductionmentioning

confidence: 99%

High-Precision Combined Tidal Forecasting Model

2019

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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.

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“…In order to choose an appropriate tide prediction approach, many effective factors should be taken into account, and a wide range of researches have focused on the same subject. Among well-known algorithms implemented to predict tide level are Kalman Filter (Yen et al 1996), Neural Network Models (Lee 2004), Harmonic Method (HM) (Doodson 1921;Schureman 1958;Godin 1972;Foreman 1979), Simplified Harmonic Method (SHM) (Whitcombe 1996), and Spectral Analysis (Vaníček 1971).…”

Section: Introductionmentioning

confidence: 99%

GIS-based seamless tide prediction framework in the Persian Gulf

2018

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Prediction of international water tides induced by gravitational centripetal forces of the Earth-Sun-Moon system is vital in various applications. Available methodologies and software that offer observation analysis and tide level prediction often support limited points of interest (mainly islands and ports), and they are mostly based on Harmonic Analysis algorithms. The present study proposes a geographical information system-based framework in order to store and retrieve tide information derived from co-tidal charts such as amplitude and phase charts. By retrieving the exact corresponding information of any point in Persian Gulf, the developed programme makes continuous tide prediction possible for every area of interest, including offshore locations, island boundaries, and alongside coasts of Persian Gulf's states. ARTICLE HISTORY

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Application of Kalman Filter to Short-Term Tide Level Prediction

Cited by 38 publications

References 7 publications

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

Estimation of Sea Level Change in the South China Sea from Satellite Altimetry Data

High-Precision Combined Tidal Forecasting Model

GIS-based seamless tide prediction framework in the Persian Gulf

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