Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Prediction of tide levels is rather an important task in determining constructions and human activities in coastal and oceanic area. Accurate predictions of tide levels could not be obtained without a large length of tide measurements by conventional methods. The Back-Propagation Neural Network (PBN) was applied to predict long term semi-diurnal tidal level. Based on the model, the different tide types for other two field data, referred as the diurnal and mixed types, are further to test the performance of PBN model. The results also present that one-year tidal level forecasting can be satisfactorily achieved using a half-month length of observed data for these two tide types.
How to cite this article:
Lee, T.L. , C.P. Tsai and R.J. Shieh . Applied the Back-propagation Neural Network to Predict Long-term Tidal Level.
DOI: https://doi.org/10.36478/ajit.2006.396.401
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2006.396.401