TY  - JOUR
T1  - Applied the Back-propagation Neural Network to Predict Long-term Tidal Level
AU - , Lee, T.L. AU - , C.P. Tsai AU - , R.J. Shieh 
JO  - Asian Journal of Information Technology
VL  - 5
IS  - 4
SP  - 396
EP  - 401
PY  - 2006
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2006.396.401
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2006.396.401
KW  - Back-propagation neural network
KW  -long term
KW  -prediction
AB  - 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.
ER  -