A prediction model of airport noise based on the dynamic ensemble learning

The prediction of airport noise plays an important role in airport noise control, flight schedule planning and surrounding designs of airport. However, the existing prediction models are complex and need so many highly accurate parameters that are monitored and collected as input of the model, hence adding difficulties to the prediction of airport noise. In order to solve these problems, this paper presents a prediction model based on the rough set and ensemble learning. Accordingly, the attributes of monitored noise data around airport is first reduced by the rough set and the subsets of attributes is produced then, the dynamic ensemble learning is used to combine base learners which are presented in three-dimensional coordinates based on the subsets of attributes. The results of experiments show that the proposed model can predict the noise of specific aircraft with full parameters being more accurately than existing models. And even if there is a lack in part of parameters, the prediction outcome of the model is able to approach the real value of airport noise while gradually increasing parameters.