准确预测机场噪声对控制噪声影响、制定航班计划和规划机场周边环境具有重要意义。而现有机场噪声预测模型复杂，需要监测和采集较多高精度数据作为输入参数，给机场噪声预测增加了困难。针对上述问题该文提出基于动态集成学习的预测模型，该模型基于粗糙集理论对历史监测数据集进行约简分组并构造属性子集，然后对由3维空间向量拟合属性子集生成的基模型进行动态集成。实验结果表明，预测参数完整时，该模型针对特定机型的预测准确性优于现有模型。即使预测参数部分缺失，预测结果也能随参数的增多逐渐逼近真实值。%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.
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