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Application of artificial neural networks in typhoon surge forecasting
- Source :
-
Ocean Engineering . Aug2007, Vol. 34 Issue 11/12, p1757-1768. 12p. - Publication Year :
- 2007
-
Abstract
- Abstract: A typhoon-surge forecasting model was developed with a back-propagation neural network (BPN) in the present paper. The typhoon''s characteristics, local meteorological conditions and typhoon surges at a considered tidal station at time t−1 and t were used as input data of the model to forecast typhoon surges at the following time. For the selection of a better forecasting model, four models (Models A–D) were tested and compared under the different composition of the above-mentioned input factors. A general evaluation index that is a composition of four performance indexes was proposed to evaluate the model''s overall performance. The result of typhoon-surge forecasting was classified into five grades: A (excellent), B (good), C (fair), D (poor) and E (bad), according to the value of the general evaluation index. Sixteen typhoon events and their corresponding typhoon surges and local meteorological conditions at Ken–fang Tidal Station in the coast of north-eastern Taiwan between 1993 and 2000 were collected, 12 of them were used in model''s calibration while the other four were used in model''s verification. The analysis of typhoon-surge forecasting results at Ken–fang tidal station show that the Model D composing 18 input factors has better performance, and that it is a suitable BPN-based model in typhoon-surge forecasting. The Model D was also 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. [Copyright &y& Elsevier]
- Subjects :
- *TYPHOONS
*ARTIFICIAL neural networks
*FORECASTING
Subjects
Details
- Language :
- English
- ISSN :
- 00298018
- Volume :
- 34
- Issue :
- 11/12
- Database :
- Academic Search Index
- Journal :
- Ocean Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 24868392
- Full Text :
- https://doi.org/10.1016/j.oceaneng.2006.09.005