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Application of artificial neural networks in tide-forecasting |
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| Authors: | T L Lee D S Jeng |
| Institution: | 1. Department of Construction and Planning, Leader University, Tainan, Taiwan 709, ROC;2. School of Engineering, Griffith University, Gold Coast Campus, PMB 50 Gold Coast Mail Centre, Queensland 9726, Australia;1. College of Physical and Environmental Oceanography, Ocean University of China, Qingdao 266100, PR China;2. Physical Oceanography Laboratory, Ocean University of China, Qingdao 266100, PR China;3. Earth Resource Technology, 20910, USA;1. Department of Structural Mechanics, Budapest University of Technology and Economics, M?egyetem rkp. 3, 1111 Budapest, Hungary;2. Department of Civil Engineering, Johns Hopkins University, Latrobe Hall 208, 3400 North Charles Street, Baltimore, MD 21218, USA;1. Budapest University of Technology and Economics, Department of Structural Mechanics, M?egyetem rkp. 3, 1111 Budapest, Hungary;2. Johns Hopkins University, Department of Civil Engineering, Latrobe Hall 205, 3400 North Charles Street, Baltimore, MD 21218, USA |
| Abstract: | An accurate tidal forecast is an important task in determining constructions and human activities in ocean environments. Conventional tidal forecasting has been based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters are required for the 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 the tidal-level using the short term measuring data. The ANN model can easily decide the unknown parameters by learning the input–output interrelation of the short-term tidal records. Three field data with three types of tides will be used to test the performance of the proposed ANN model. The numerical results indicate that the hourly tidal levels over a long duration can be predicted using a short-term hourly tidal record. |
| Keywords: | Artificial Neural Network Tide forecasting Back-propagation neural network |
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