基于混核极限学习机的道路高排放源识别方法

On-road high-emitter identification method based on mixed kernel extreme learning machine

由于道路高排放源所产生的污染气体对环境危害巨大,因此实现对高排放源的准确识别具有重要意义。而传统的基于限值划分的识别方法及新兴的人工智能识别方法在模型选择、评价指标、识别性能等方面都存在一定的改进空间,因此针对以上问题,提出一种基于混核极限学习机的道路高排放源识别方法。该方法使用道路遥感监测设备获取的移动源遥测数据,在核极限学习机的基础上融合不同核函数,可提升模型鲁棒性及道路高排放源识别性能。针对合肥市蜀山区真实道路遥测数据上的分析结果表明,该方法相比于其他方法具有较高的F1分数以及较低的漏报率、虚警率,证实了该方法在高排放源识别中的有效性。因此,该方法有助于对交通路网中高排放车辆进行高效识别,为进一步提升城市空气质量提供支撑。

Since the pollution gases produced by on-road high-emitters are significantly harmful to the environment,it is of great significance to identify on-road high-emitters accurately.However,there is still a relatively large promotion space for identifying high-emitters in model selection,evaluation metrics,recognition performance,and other aspects,for both traditional cutpoint-based methods and emerging artificial intelligence-based methods.Therefore,to address the above issues,a method for on-road high-emitters identification is proposed based on mixed kernel extreme learning machine.The method uses mobile source telemetry data obtained from on-road remote sensing detection equipment,and integrates different kernel functions on the basis of kernel extreme learning machine,which can improve the robustness of the model and the recognition performance of on-road high-emitters.The experimental results on remote sensing data collected from the traffic network in Shushan District of Hefei City,China,show that,compared with the other methods,the proposed method has a higher F1-Score,lower missing alarm rate and false alarm rate,which confirms the effectiveness of the method in high-emitter identification.It is indicated that the proposed method can help to identify high-emitters in the traffic road network efficiently and provide basic support for further improving urban air quality.