Development of PM2.5 Forecast Model Combining ConvLSTM and DNN in Seoul

• Published in: Atmosphere 2024-11, Vol.15 (11), p.1276
• Main Authors: Koo, Ji-Seok, Wang, Kyung-Hui, Yun, Hui-Young, Kwon, Hee-Yong, Koo, Youn-Seo
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; Chronic obstructive pulmonary disease; convLSTM; DNN; Extended forecasting; False alarms; forecast; Forecast accuracy; Forecasting; Forecasting models; hybrid model; Long short-term memory; Machine learning; Neural networks; Outdoor air quality; Particulate matter; PM2.5; Pollutants; Pollution; Pollution dispersion; Pollution transport; Public health; Time series
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos15111276

Accurate prediction of PM2.5 concentrations is essential for public health management, especially in areas affected by long-range pollutant transport. This study presents a hybrid model combining convolutional long short-term memory (ConvLSTM) and deep neural networks (DNNs) to enhance PM2.5 forecasting in Seoul, South Korea. The hybrid model leverages ConvLSTM’s ability to capture spatiotemporal dependencies and DNN’s strength in feature extraction, enabling it to outperform standalone CMAQ and DNN models. For the T1 forecast (6 h averages), the ConvLSTM-DNN model exhibited superior performance, with an RMSE of 7.2 µg/m3 compared to DNN’s 8.5 µg/m3 and CMAQ’s 10.1 µg/m3. The model also maintained high categorical accuracy (ACC) and probability of detection (POD) for critical PM2.5 levels while reducing false alarms (FARs), particularly in bad and very bad events. Although its performance decreases over extended forecast periods, the ConvLSTM-DNN model demonstrates its utility as a robust forecasting tool. Future work will focus on optimizing the network structure to improve long-term forecast accuracy.