|Author's Email Address
||This thesis had been viewed 5377 times. Download 1874 times.|
||Marine Environment and Engineering|
|Type of Document
||Apply Neural Network Techniques for Storm Surge Prediction|
|Date of Defense
|| Neural Network
||Taiwan is often threaten by typhoon during summer and autumn. The surges brought by theses typhoons not only cause human lives in danger, but also cause severe floods in coastal area. Storm surge prediction remains still a complex coastal engineering problem to solve since lots of parameters may affect the predictions. The purpose of this study is to predict storm surges using an Artificial Neural Network (ANN). A non-linear hidden-layer forward feeding neural network using back-propagation learning algorithms was developed. The study included a detailed analysis the factors may affect the predictions. The factors were obtained from the formulation of storm surge discrepancies after Horikawa (1987). Storm surge behaviors may vary from different geographical locations and weather conditions. A correlation analysis of the parameters was carried out first to pick up those factors shown high correlations as input parameters for establishing the typhoon surge predictions.|
The applications started with collecting tide and meteorological data (wind speed, wind direction and pressure) of Dapeng Bay and Kaohsiung harbor. A harmonic analysis was utilized to identify surge deviations. The surge deviation recorded at Dapeng Bay was found higher then Kaohsiung harbor for the same typhoon events. Correlation analysis has shown positive correlations between wind field, both wind speed and direction, and the associated storm surge deviations at Dapeng Bay. Correlation coefficients (CC) 0.6702 and 0.58 were found respectively. The variation of atmospheric pressure during typhoons is found with positive correlation too (i.e. CC=0.3626). Whereas the analysis has shown that the surges at Kaohsiung harbor were only sensitive to wind speed (CC=0.3723), while the correlation coefficients of the wind direction (CC=-0.1559) and atmospheric pressure (CC= -0.0337) are low. The wind direction, wind speed and atmospheric pressure variation were then used as input parameters for the training and predictions.
An optimum network structure was defined using the Dapeng Bay data. The best results were obtained by using wind speed, wind direction and pressure variation as input parameters. The ANN model can predict the surge deviation better if the empirical mode decomposition (EMD) method was used for training.
||C.P Tsai - chair|
Laurence Z. H. Chuang - co-chair
Jason C.S. Yu - advisor
indicate accessible in a year|
|Date of Submission