## 1 Introduction  
Tropical storms frequently occur over tropical oceans, and they are highly likely to evolve into a typhoon event when the wind speed is greater than 118 km/h [1]. Owing to the high wind speed, they usually cause extensive damage to engineering structures and infrastructure facilities. Specifically, wind-sensitive structures, e.g., long-span bridges, high-rise buildings, and large-expanse roofs, may suffer from high risks to structural safety [2, 3]. Hence, tropical storms attract significant attention in the view of structural safety and serviceability in coastal regions. In light of the destructive power and inevitable occurrences of tropical storms each year, the prediction of wind speeds based on recorded data becomes an important topic in the area of structural wind engineering. If the wind speed can be accurately forecasted in advance, emergency measures can be taken to reduce the losses as far as possible. Effective approaches are thus essential for the short-term prediction of tropical storms. In recent decades, various approaches have been proposed for the prediction of wind speeds with the purpose of wind energy forecasting or disaster prevention [4, 5]. The approaches can be classified into two categories, including the physical method and the statistical method [6, 7]. The physical method forecasts the wind speed based on formulas interpreting the laws of physics and considers the site conditions, including meteorology (temperature, pressure, humidity, and so on) and terrain effects [8, 9, 10]. Nevertheless, the calculations used in solving the associated governing equations are so complicated that they require substantial computational resources, which makes the simulation extremely time-consuming. The physical method is thus deemed to not be appropriate for the purposes of early warning. In contrast, the statistical method employs time-series models or black-box models, established based on historical data, to predict the future wind speed. This approach tacitly assumes that the future data have a linear or nonlinear relationship with the previous data. Compared with the physical method, the statistical method does not require many computational resources and is suitable for short-term forecasting. The time-series models frequently used in the statistical method mainly include the autoregressive moving average (ARMA) model [11; 12; 13; 14], auto-regressive integrated moving average (ARIMA) model [15; 16; 17], and other variants like the fractional ARIMA model [18] and vector autoregressive model [19; 20]. These models have extensive applications in the forecasting of wind speeds, and effective predictions can be obtained when distinct linear features exist in the data. When it comes to the case with strong nonlinearities, nonlinear models are required to describe the connection between input and output data, and the prediction accuracy is usually improved compared with the results of linear models. To characterize the nonlinear features, the artificial intelligence (AI) technique offers effective networks that can establish a black-box model for the input and output data. The function of the black-box model is similar to the time-series model, but the nonlinear patterns between future data and historical data can be represented. AI approaches mainly include back-propagation neural network (BPNN) [21; 22], long short-term memory (LSTM) network [23; 24], gated recurrent units (GRUs) network [25; 26; 27], support vector regression (SVR) [28; 29; 30], and other related networks [31; 32; 33]. Although, in some applications, the nonlinear model displays better performance compared with the time-series model, the training of the network is time-consuming and the trained network is sensitive to the preset parameters. The aforementioned approaches have been widely used in the forecasting of conventional boundary-layer winds, but their applicability in tropical storms is rarely reported in the literature.