On-road high-emitter identification method based on mixed kernel extreme learning machine
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.
万方数据
维普
