Comparison of LSTM and Bi-LSTM Models for Predicting PM2.5 Concentration in Seoul

The importance of predicting fine particulate matter (PM_2.5) concentrations has grown significantly due to deteriorating urban air quality. Seoul presents unique modeling challenges due to its intensive industrial activities, high population density, significant data variability between monitoring stations, and numerous missing values. This study analyzes and compares the predictive performance of LSTM (Long Short-Term Memory) and Bi-LSTM (Bidirectional LSTM) models using hourly PM_2.5 concentration data collected from 25 monitoring stations in Seoul from 2018 to 2022.

In the data preprocessing phase, we employed the MICE (Multiple Imputation by Chained Equations) method to handle missing values, which effectively preserved the data's structural characteristics by considering inter-variable relationships. The predictive performance of both models was evaluated using metrics such as RMSE (Root Mean Square Error) and MAE (Mean Absolute Error). While LSTM focuses on forward learning, Bi-LSTM can capture complex time series patterns by utilizing both forward and backward information.

The results demonstrated that Bi-LSTM effectively captured complex time series patterns through its bidirectional learning structure, and optimal learning rates and dropout ratios were determined through various parameter tuning experiments. These findings present the characteristics and potential applications of both models in PM_2.5 concentration prediction, expected to contribute to improved air quality forecasting systems and policy decision support.

Future research will focus on enhancing model accuracy through the implementation of additional algorithms and exploring applications for PM_2.5 prediction in other cities. This aims to increase the spatial resolution of air pollution prediction and contribute to broader air quality improvements.

Acknowledgement 

"This research was supported by Particulate Matter Management Specialized Graduate Program through the Korea Environmental Industry & Technology Institute(KEITI) funded by the Ministry of Environment(MOE)"