Neural Network model imposed membership functions for typhoon waves

• Main Authors: Lin, Shian-Ming, 林賢銘
• Other Authors: Chang, Hsien-Kuo
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/14083945814076534223

碩士 === 國立交通大學 === 土木工程學系 === 99 === The thesis is to develop an Neural Network (ANN) model imposed by membership functions to estimate the typhoon waves. Wave data observed by the Harbor and Marine Technology Center during 2000 to 2009 at Anping harbor and typhoon data collected by JMA RSMC-Tokyo Center were used to train the ANN model. The validity of the proposed ANN model is verified by measured wave heights in the test stage. Five parameters including the distance from typhoon center to the interesting point (D), the azimuth between typhoon center and the interesting point (θ1), position angle in the typhoon (θ3), the wind velocity of the interesting point (V) and its responding wind direction (Vdeg), were selected in the input layer of ANN. Low correlation coefficients between some input parameters and wave heights indicating insignificant weighting to the model doesn’t illustrate basic physical interpretation. Gauss membership functions are used in the paper to transform three angle parameters, that are θ1, θ3 and Vdeg, to remedy the disadvantages of original parameters. The corrected ANN model promotes the capacity of estimating wave heights in the test stage by 7% than the original model. An extra procedure of validation is set in the training stage can increase the model performance by 9% accuracy than the original model. The proposed ANN wave model was examined to have higher accuracy on calculating typhoon waves than traditional empirical formula. Due to good estimation on typhoon waves by the proposed ANN model, the proposed method can be applied to other positions for establishing ANN forecasting wave models to provide wave information for navigation and marine activities.