Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
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An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large amount of harmonic constants are required for the accurate prediction of a long-term tidal level with harmonic analysis. Unlike conventional harmonic analysis, this paper presents an artificial neural network (ANN) model for forecasting tidal-level using the short term measuring data. The ANN model can easily decide the harmonic constants by learning the input-output interrelation of the short-term tidal records. A case study with the data at Tanshui Harbour (in Northern Taiwan) is considered to test the performance of the proposed model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record.