Typhoon surge forecasting with artificial back-propagation neural networks

C. D. Jan, C. M. Tseng, J. S. Wang, C. M. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A typhoon-surge forecasting model was developed with the application of the back-propagation neural network (BPN) in the present paper. This artificial neural network model forecasts the hourly time series of typhoon surge variation based on a set of input data including typhoon's characteristics, local meteorological conditions and typhoon surges at a considered tidal station. For selecting a better forecasting model, four models (Models A, B, C, and D) were tested and compared under the different composition of input factors. A general evaluation index that is a composition of four performance indexes was proposed to evaluate the model's overall performance. Tested results show that Model D composing 18 input factors has best performance among the four models, The Model D was then applied to typhoon-surge forecasting at Cheng-kung Tidal Station in south-eastern coast of Taiwan and at Tung-shih Tidal Station in the coast of south-western Taiwan. Results show that the application of Model D in typhoon-surge forecasting at Cheng-kung Tidal Station has better performance than that at Tung-shih Tidal Station.

Original languageEnglish
Title of host publication2007 16th IEEE International Symposium on the Applications of Ferroelectrics, ISAF
PublisherIEEE Computer Society
ISBN (Print)1424401380, 9781424401383
Publication statusPublished - 2006 Jan 1
EventOCEANS 2006 - Asia Pacific - , Singapore
Duration: 2007 May 162007 May 19

Publication series

NameOCEANS 2006 - Asia Pacific


OtherOCEANS 2006 - Asia Pacific

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Ocean Engineering


Dive into the research topics of 'Typhoon surge forecasting with artificial back-propagation neural networks'. Together they form a unique fingerprint.

Cite this