SUMMARY
Taiwan is located on a route where typhoons often strike. Each year, the strong winds accompanying typhoons are a substantial threat and cause significant damage. However, because the terrains of high mountains in Taiwan vary greatly, when a typhoon passes the Central Mountain Range (CMR), the wind speed of typhoons becomes difficult to predict. This research had two primary objectives: (1) to develop data-driven techniques and a powerful artificial neural network (ANN) model to predict the highly complex nonlinear wind systems in western Taiwan; and, (2) to investigate the accuracy of wind speed predictions at various locations and for various durations in western Taiwan when the track of westward typhoons is affected by the complex geographical shelters and disturbances of the CMR. This study developed a typhoon wind speed prediction model that evaluated various typhoon tracks (covering Type 2, Type 3, and Type 4 tracks, as defined by the Central Weather Bureau), and evaluated the prediction accuracy at Hsinchu, Wuqi, and Kaohsiung Stations in western Taiwan. Back propagation neural networks (BPNNs) were employed to establish wind speed prediction models, and a linear regression model was adopted as the benchmark to evaluate the strengths and weaknesses of the BPNNs. The results were as follows: (1) The BPNNs generally had favorable performance in predicting wind speeds and their performances were superior to linear regressions; (2) when absolute errors were adopted to evaluate the prediction performances, the predictions at Hsinchu Station were the most accurate, whereas those at Wuqi Station were the least accurate; however, when relative errors were adopted, the predictions at Hsinchu Station were again the most accurate, whereas those at Kaohsiung were the least accurate; and, (3) regarding the relative error rates for the maximum wind speed of Types 2, 3, and 4 typhoons, Wuqi, Kaohsiung, and Wuqi had the most accurate performance, respectively; as for maximum wind time error (ETM) for Types 2, 3, and 4 typhoons, Kaohsiung, Wuqi, and Wuqi correspondingly performed the most favorably.