Incorporating temporal multi-head self-attention convolutional networks and LightGBM for indoor air quality prediction

Abstract

Friction between subway wheels and tracks and inadequate combustion of fuel are causes of respirable particulate matter. Restricted underground ventilation and high population density make it difficult for particulate matter to dissipate, posing a threat to human health. An effective data-driven model for indoor pollutant prediction can enhance preparedness for high pollution situations. This study introduces a feature extraction method that combines kernel principal component analysis with max-relevance and min-redundancy algorithm. The temporal convolutional network, enhanced with a multi-head self-attention mechanism, adeptly captures time series features and effectively manages attention weight allocation. Additionally, the incorporation of the light gradient boosting machine method enhances overall efficiency. The proposed framework for PM2.5 concentration prediction was employed to a high-traffic subway station in Seoul. In the test set, the model demonstrated strong performance with evaluation metrics including an R2 value of 0.92, RMSE of 6.02 μg/m3, MAE of 4.36, and MAPE of 20.58 μg/m3. Compared to the conventional LSTM, the proposed method reduces the RMSE by 20.5% and the MAPE by 49.05%. Notably, the model we propose demonstrates superior capabilities in managing large datasets and offers enhanced predictive accuracy compared to baseline models. It effectively addresses the limitations observed in models like LSTM, which often struggle with adequately capturing feature information, and overcomes the generalization weaknesses inherent in models such as the Transformer. This advancement significantly boosts the efficiency of environmental monitoring and fosters both automation and intelligence in the analysis of environmental data.