BACK-PROPAGATION NEURAL NETWORK IN TIDAL-LEVEL FORECASTING

Reliability of tidal-level forecasting is essential for structure installation and human activities in the marine environment. This paper reports an application of the artificial neural network with backpropagation procedures for accurate forecast of tidal-level variations. Unlike the conventional harmonic analysis, this neural network model forecasts the time series of tidal levels directly using a learning process based on a set of previous data. Two sets of field data with diurnal and semidiurnal tide, respectively, were used to test the performance of the neural network model. Results indicate that the hourly tidal levels over a long duration can be efficiently predicted using only a very short-term hourly tidal record.

• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0733950X
• Supplemental Notes:
  • This work is based on the project "Utilization of Coastal Zone" supported by the National Science Council, Taiwan, under Grant No. NSC87-2611-E-005-004.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Tsai, C-P
  • Lee, T-L
• Publication Date: 1999-7

Language

• English

Media Info

• Features: Appendices; Figures; References; Tables;
• Pagination: p. 195-202
• Serial:
  • Journal of Waterway, Port, Coastal and Ocean Engineering
  • Volume: 125
  • Issue Number: 4
  • Publisher: American Society of Civil Engineers
  • ISSN: 0733-950X
  • EISSN: 1943-5460
  • Serial URL: http://ascelibrary.org/journal/jwped5

Subject/Index Terms

• TRT Terms: Algorithms; Backpropagation; Forecasting; Mathematical models; Neural networks; Reliability; Tidal currents; Tides; Time series
• Subject Areas: Marine Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 00766165
• Record Type: Publication
• Contract Numbers: NSC87-2611-E-005-004
• Files: TRIS
• Created Date: Jul 12 1999 12:00AM