Enhanced TSMixer Model for the Prediction and Control of Particulate Matter

Chaoqiong Yang, Haoru Li, Yue Ma, Yubin Huang and Xianghua Chu ()
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Chaoqiong Yang: Shenzhen Ecological Environment Monitoring Station, Shenzhen 518060, China
Haoru Li: College of Management, Shenzhen University, Shenzhen 518060, China
Yue Ma: College of Management, Shenzhen University, Shenzhen 518060, China
Yubin Huang: College of Management, Shenzhen University, Shenzhen 518060, China
Xianghua Chu: College of Management, Shenzhen University, Shenzhen 518060, China

Sustainability, 2025, vol. 17, issue 7, 1-17

Abstract: This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing the temporal evolution of PM 2.5 concentrations from multivariate monitoring data, the model demonstrates significant prediction capabilities while maintaining consistency with observed pollutant transport characteristics in the urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance the predictive capability for complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused to achieve high-precision predictions of PM 2.5 concentrations through a multivariate time-series forecasting model. An asymmetric penalty mechanism is added to dynamically optimize the loss function. Experimental results indicate that the proposed E-TSMixer model achieves higher accuracy for the prediction of PM 2.5 , which significantly outperforms the traditional models. Additionally, an intelligent dual regulation of fixed and dynamic threshold model is introduced and combined with E-TSMixer for the decision-making model of the real-time adjustments of the frequency, routes, and timing of water truck operation in practice.

Keywords: multivariate time-series forecasting; particulate matter prediction; TSMixer Model; dynamic decision-making mechanism; intelligent road dust control (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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