TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations

被引：0
|
作者：
Almujibah, Hamad [1 ]
Almaliki, Abdulrazak H. [1 ]
Mongina Matara, Caroline [2 ]
Abdallah Mohammed Elhassan, Adil [1 ]
Alla Adam Mohamed, Khalaf [3 ]
Bakri, Mudthir [4 ]
Khattak, Afaq [5 ]
机构：
[1] Taif University, College of Engineering, Department of Civil Engineering, Taif,21944, Saudi Arabia
[2] Technical University of Kenya, Department of Civil and Construction Engineering, Nairobi,00200, Kenya
[3] Bisha University, College of Engineering, Department of Civil Engineering, Bisha,61361, Saudi Arabia
[4] Qassim University, College of Engineering, Department of Civil Engineering, Buraidah,52571, Saudi Arabia
[5] Tongji University, College of Transportation Engineering, Jiading, Shanghai,201804, China
来源：
IEEE Access | 2024年 / 12卷
关键词：
This study proposes a novel hybrid approach for estimating and analyzing vehicle-related PM2.5 concentrations. The framework integrates the Local Cascade Ensemble (LCE) model; optimized using the Tree-structured Parzen Estimator (TPE) strategy; with SHapley Additive exPlanations (SHAP) to enhance interpretability. It utilizes datasets comprising air quality; meteorological; and traffic data collected from strategically placed sensors along the Nairobi Expressway. Key parameters include hourly traffic volume; average vehicle speed; humidity; wind speed; and temperature. The TPE-tuned LCE model outperformed benchmark algorithms including Random Forest (RF); Extreme Gradient Boosting (XGBoost); Light Gradient Boosting Machine (LightGBM); Adaptive Boosting (AdaBoost); and Multiple Linear Regression (MLR) achieved the lowest Mean Absolute Error (MAE) of 1.94; Mean Squared Error (MSE) of 21.50; Root Mean Squared Error (RMSE) of 4.64; Residual Standard Ratio (RSR) of 0.38; and the highest Coefficient of Determination (R2) of 0.87. SHAP analysis of TPE-tuned LCE model identified location; and wind speed as the most influential predictors of PM2.5 levels. This hybrid framework delivers robust predictive accuracy and actionable insights; making it a valuable tool for effective environmental management and policy making. © 2013 IEEE;
D O I：
10.1109/ACCESS.2024.3505116
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页码：179219 / 179234
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