  An extreme wind speed estimation method that considers wind hazard climate
types is critical for design wind load calculation for building structures
affected by mixed climates. However, it is very difficult to obtain wind hazard
climate types from meteorological data records, because they restrict the
application of extreme wind speed estimation in mixed climates. This paper
first proposes a wind hazard type identification algorithm based on a numerical
pattern recognition method that utilizes feature extraction and generalization.
Next, it compares six commonly used machine learning models using K-fold
cross-validation. Finally, it takes meteorological data from three locations
near the southeast coast of China as examples to examine the algorithm
performance. Based on classification results, the extreme wind speeds
calculated based on mixed wind hazard types is compared with those obtained
from conventional methods, and the effects on structural design for different
return periods are discussed.
