Enhanced TSMixer Model for the Prediction and Control of Particulate Matter DOI Open Access

Chaoqiong Yang,

Haoru Li,

Yue Ma

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 2933 - 2933

Published: March 26, 2025

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing temporal evolution PM2.5 concentrations from multivariate monitoring data, model demonstrates significant capabilities while maintaining consistency with observed pollutant transport characteristics in urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance predictive capability complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused achieve high-precision predictions through time-series forecasting model. An asymmetric penalty mechanism added dynamically optimize loss function. Experimental results indicate that E-TSMixer achieves higher accuracy PM2.5, which significantly outperforms traditional models. Additionally, intelligent dual regulation fixed dynamic threshold introduced combined decision-making real-time adjustments frequency, routes, timing water truck operation practice.

Language: Английский

Enhanced TSMixer Model for the Prediction and Control of Particulate Matter DOI Open Access

Chaoqiong Yang,

Haoru Li,

Yue Ma

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 2933 - 2933

Published: March 26, 2025

This study presents an improved deep-learning model, termed Enhanced Time Series Mixer (E-TSMixer), for the prediction of particulate matter. By analyzing temporal evolution PM2.5 concentrations from multivariate monitoring data, model demonstrates significant capabilities while maintaining consistency with observed pollutant transport characteristics in urban boundary layer. In E-TSMixer, a fully connected output layer is proposed to enhance predictive capability complex spatiotemporal dependencies. The relevant data on air quality and traffic flow are fused achieve high-precision predictions through time-series forecasting model. An asymmetric penalty mechanism added dynamically optimize loss function. Experimental results indicate that E-TSMixer achieves higher accuracy PM2.5, which significantly outperforms traditional models. Additionally, intelligent dual regulation fixed dynamic threshold introduced combined decision-making real-time adjustments frequency, routes, timing water truck operation practice.

Language: Английский

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