单飞行事件噪声关联规则挖掘及信念网络表示

Exploration and belief network representation of association rules between noise event sets for single flight

挖掘各种噪声影响因素与机场噪声之间的关联规则,能够为机场经营者制定有效的降噪措施提供科学依据。先将FP-Growth算法与数据立方体相结合,构造FP-Growth-Cube算法挖掘单飞行事件噪声与各噪声影响因素之间的关联规则,然后通过信念网络可视化表示关联规则。此方法不但直观、清晰,而且克服了关联规则不能表达不同规则之间联系的弱点,图形化地综合表示各因素对噪声的影响。最后,实验从降噪措施之一的机型选择角度,与权威噪声预测软件INM对比,验证了该方法的可行性和有效性。

Exploration of the relation ship between factors and airport noises can help airport operators adopt effective noise reduction measures. FP-growth-cube algorithm is used to find the association rules between noise and factors. The association rules are represented in the form of belief network. The representation of association rules is intuitive and clear, which can overcome the weakness of association rules, which can not express all airport noise influencial factors synthetically. Experimental results prove the feasibility and effectiveness of the method from the viewpoint of one noise reduction measure-aircraft type selection.