Nearshore wave height hindcasting at an arbitrary point by using a combined numerical-ANN model during typhoons

• Main Authors: Hsieh, Chia-Jung, 謝家榮
• Other Authors: Wei, Chih-Chiang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/pz76p8

碩士 === 國立臺灣海洋大學 === 海洋環境資訊系 === 106 === Taiwan is located at the junction of tropical and subtropical Pacific Ocean. Typhoons often occur in summer and autumn under the conditions of ocean and high temperature. The northeastern of Taiwan often bears the brunt and comes the most powerful storms. Because there is no mountain barrier in northeastern of Taiwan like western, typhoons can invade with impunity. However, the current buoys are seldom in Northeastern Taiwan and the information of wave height without buoys is limited. Thus, the wave heights of typhoons at an arbitrary point are determined difficultly. Therefore, adding the wave height simulated by the numerical model as a supplementary data for prediction. The development of the numerical model has been very advanced. The prediction of the wave height during the typhoons also tend to be accurate. However, the disadvantage is that the numerical simulation requires a long time and it can’t be used for real-time prediction. This study designed a three-stage research process and developed a method of artificial neural network (ANN) combined with numerical model that can simultaneously have the numerical simulation accuracy and the efficiency of ANN to achieve real-time wave height forecasting of an arbitrary point at northeastern of Taiwan in typhoon period. The collected data are from 2005 to 2015, including the weather data of the weather station of the Central Meteorological Bureau, the buoy observation data and the typhoon warning information. In the first stage, we used the machine learning methods to establish the wave forecasting model during the typhoon periods. In the second stage, we used the SWAN numerical model to simulate typhoon waves. The SWAN model used a wide range of calculations as boundary conditions, and then did small-scale calculations. Then, the SWAN model is used to simulate the typhoon waves of the nearshore. In the third stage, we predicted the typhoon wave height at an arbitrary point. Assuming that there is no measured data for the learning target of the ANN model. The wave height results simulated by SWAN model were used to develop the combined ANN with SWAN models. The merits of the combined model are having physical meaning of SWAN numerical model and calculating ANN rapidly. The findings revealed that: (1) The prediction error of Longdong area was smaller and had a higher correlation than that of Guishan Island. (2) Wave height less than 2 m was defined as small wave; Wave height between 2 m and 3 m was defined as medium wave; Wave height above 3 m was defined as large wave. Prediction of small wave and large wave in the area of Longdong area were better. Prediction of small wave in the area of Guishan Island were better. (3) The overall correlation of ANN model between the predicted value and the measured value was relatively large and the RMSE was small, followed by SWAN model, and the worst is the coupled model.