Identification of high-emission heavy-duty diesel vehicles based on multigrained cascade forest
Vehicle exhaust emissions are a major source of air pollution.On-board diagnostic(OBD)systems are important regulatory tools for vehicle emissions because they can directly access key in-formation related to nitrogen oxide(NOx)emissions.However,owing to data unavailability and qual-ity issues in OBD systems,accurately assessing the NOx emission levels of vehicles and effectively screening high-emission vehicles are challenging.This study proposes a method for screening high-emission heavy-duty diesel vehicles(HDDVs)using the multigrained cascade forest model.First,the Gumbel distribution is used to fit the probability density distribution of the ratio to determine the high-emission threshold and label high-emission records.Subsequently,the multicollinearity test is performed in conjunction with the entropy method to determine the optimal feature subsets.Next,the synthetic minority oversampling technique(SMOTE)is used to address the imbalance between high-emitting and clean samples.Finally,the multigrained cascade forest model is constructed to classify data with emissions that exceed the standards.Comparative-analysis experiments verify the effective-ness and applicability of the model in identifying high NOx emissions from HDDVs,thus enhancing the feasibility of identifying high-emission vehicles and providing reliable data support for the pre-cise regulation of vehicle emissions.
information technologyhigh-emitting vehicle identificationmulti-Grained Cascade Forestheavy-duty diesel vehicleon-board diagnostics systemGumbel distribution
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