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Due to lack of enough available sample of some regions (such as regional airports in Hainan province), it is difficult to establish a predictive model for the passenger amount of aviation transport using traditional statistical methods. After we analyze previous related works, we propose to combine hierarchical clustering and support vector regression (SVR) methods to obtain a predictive model. Firstly, we find the development level is similar to Hainan province (Guangxi province) using hierarchical cluster method. Secondly, we obtain the optimal SVR model (C = 1024, g = 0.0013811) by means of k-fold cross validation (k-CV) based on the sufficient samples of Guangxi province. After comparing the SVR model with the multiple linear regression prediction method, the result shows that has better prediction accuracy. Finally, the SVR model applied for predicting the passengers transported by regional airports from 2018 to 2020 in Hainan province, which could provide a decision-making reference and reliable theoretical basis for constructing of the regional airports in the future.
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