| Summary: | 博士 === 國立交通大學 === 土木工程系所 === 94 === This study analyzes, tests, and compares different models with hybrid structures based on neural networks and fuzzy rules. It is found that the forecasting results of the FN2 typhoon wave model, which combines neural network, fuzzy rules and multi-trend transfer functions, are the best. In addition to experiential and numerical methods, this study provides another fast and reliable method to construct the typhoon wave forecasting model.
FN2 model uses the typhoon category and route data issued by JTWC and the Holland wind model to build up the input data. Suitable parameters selected include the wind velocity at 10m high above the sea surface, the distance from typhoon center to the interesting point, the azimuth between typhoon center and the interesting point, the angle of an interesting point in a typhoon between the typhoon moving direction and the radial direction from the typhoon’s eye to that point.This model evaluates the influence generated by different typhoon routes through the 24-hour time-delay input data. Then, it applies the adjusted multi-trend transfer function to decrease the errors resulted from the shortage of learning data and to enhance its learning efficiency and accuracy. Finally, FN2 applies fuzzy rules and land attributes to build up the fuzzy membership functions so as to adjust the environment influence on typhoon waves.
This study takes Hua-Lian Harbor in the eastern waters of Taiwan as the subject and applies its related observed data for verification. The construction of Hua-Lian Harbor typhoon wave forecasting model is based on the FN2 structure. The results show that the model’s capability for simulating the most height and scale of the typhoon waves is quite satisfied. Besides, FN2 can also be extended to the points with similar land attributes neighboring to Hua-Lien Harbor.
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