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Tide Prediction Using Neural Networks
Author(s) -
Deo M. C.,
Chaudhari Girish
Publication year - 1998
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/0885-9507.00091
Subject(s) - artificial neural network , correlation coefficient , cascade , linear regression , training (meteorology) , computer science , field (mathematics) , correlation , statistics , artificial intelligence , machine learning , mathematics , meteorology , engineering , geography , geometry , chemical engineering , pure mathematics
Prediction of tides at a subordinate station located in the interior of an estuary or a bay is normally done by applying an empirical correction factor to observations at some standard or reference station. This paper presents an objective way to do so with the help of the neural network technique. In complex field conditions this approach may look more attractive to apply. Prediction of high water and low water levels as well as that of continuous tidal curves is made at three different locations. The networks involved are trained using alternative training algorithms. Testing of the networks indicated satisfactory reproduction of actual observations. This was further confirmed by a high value of the accompanying correlation coefficient. Such a correlation was better than the one obtained through use of the statistical linear regression model. The training algorithm of cascade correlation involved the lowest training time and hence is found to be more suitable for adaptive training purpose.