Research on data calibration model of micro air quality monitoring station based on particle swarm optimization algorithm

Chen Yang; Ge Ren; Hong Lin

doi:10.1117/12.3004038

11 October 2023 Research on data calibration model of micro air quality monitoring station based on particle swarm optimization algorithm

Chen Yang, Ge Ren, Hong Lin

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128000U (2023) https://doi.org/10.1117/12.3004038
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Given the large number of deployed air microstates but their low accuracy, it is not possible to accurately provide the true values of air pollutants. A data correction model (F-LSTM) combining Deep Neural Network (DNN) and Short-Term Memory neural network (LSTM) was proposed, and Particle Swarm Optimization (PSO) was added to obtain the true concentration values of PM2.5, PM10, nitrogen dioxide, sulfur dioxide, carbon monoxide and ozone. Use data from five air microstates in Zhengzhou High tech Zone in 2021 for validation. The results show that compared with LSTM and F-LSTM, the root mean square error and absolute mean error of the improved hybrid model (PSO-FLSTM) of particle swarm optimization are significantly reduced.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chen Yang, Ge Ren, and Hong Lin "Research on data calibration model of micro air quality monitoring station based on particle swarm optimization algorithm", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128000U (11 October 2023); https://doi.org/10.1117/12.3004038

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