基于随机森林的公路隧道CO气体浓度预测模型研究
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1.河北省高速公路延崇筹建处;2.河北工业大学土木与交通学院

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X830

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Prediction model of CO concentration in highway tunnel based on Random Forest
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Hebei Province Expressway Yanchong Construction Office

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    摘要:

    汽车尾气的主要成分是CO气体,是公路隧道通风设计的一项重要参数。准确、快速地预测隧道内CO气体浓度,能够为隧道通风控制提供有力参考,有助于CO气体浓度的及时控制,对保障隧道内人员的健康、安全和隧道绿色节能十分必要。采用公路隧道实地监测CO气体浓度数据,建立了以监测点位置、交通量、车速、风速为输入特征的公路隧道CO气体浓度预测随机森林模型。通过整理3 300 m长隧道CO气体浓度数据,对比了CO气体浓度实测数据与模型预测值,验证了模型的预测精度。结果表明,基于随机森林建立的CO气体浓度预测模型具有良好的预测精度,能够准确地预测隧道内CO气体浓度,测试集的RMSE和R2分别为0.3979和0.9437;该预测模型性能显著优于线性回归模型和支持向量机模型;预测模型能够推广应用于其他隧道的CO气体浓度预测,对应的RMSE和R2分别为0.7276和0.7295,可以在已知测点位置、交通量、车速、风速的情况下预判隧道内CO气体浓度,为隧道通风控制或安全预警提供数据参考;特征重要性分析结果显示,测点位置对隧道内CO浓度的影响最大,在隧道出口处CO气体浓度值最高;随着风速的增大,隧道内CO气体浓度逐渐减小。

    Abstract:

    Carbon monoxide (CO) is the main component of vehicle exhaust in highway tunnels, which is one of the important parameters for the design of ventilation in road tunnels. It is very necessary to predict the CO concentration accurately and quickly to provide a reference for tunnel ventilation system control and mitigating the concentration of CO timely and to ensure the health and safety of the people in the tunnel. At the same time, it is beneficial to the green and energy-saving operation of the tunnel. Based on the field monitoring data of the road tunnel, Random Forest (RF) model of CO concentration prediction was established with the four influencing factors, i.e., monitor position, traffic, vehicle speed, wind speed. The model was employed to predict the concentration of CO in a 3 300-long road tunnel and the predictive values were compared with the real values. The results show that the CO concentration prediction model based on Random Forest has good prediction accuracy, which can predict the concentration of CO accurately. The RMSE and R2 of the testing dataset is 0.3979 and 0.9437, respectively, which is significantly better than those of the linear regression model and Support Vector Machine (SVM) model. The prediction model can be extended to predict the CO concentration of another tunnel. The RMSE and R2 of the validation dataset is 0.7276 and 0.7295, respectively. That is, the CO concentration in the tunnel can be predicted under the condition of known monitor position, traffic, vehicle speed, wind speed. The model provides a reference for tunnel ventilation system control or safety warning. The position of monitor point has the greatest influence on the CO concentration in the tunnel, and the CO gas concentration is the highest at the exit of the tunnel. With the increase of wind speed, the concentration of CO gas in the tunnel gradually decreases.

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张志刚,徐莹,张锦秋,等. 基于随机森林的公路隧道CO气体浓度预测模型研究[J]. 科学技术与工程, , ():

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  • 收稿日期:2021-11-03
  • 最后修改日期:2022-03-10
  • 录用日期:2022-03-28
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