A New Method for Predicting SO2 Density Using Multi-Step Data with Encoder-Decoder LSTM

This study investigates the use of a Multi-Step Output Encoder-Decoder Long Short-Term Memory (LSTM) model for predicting sulfur dioxide (SO2) density. LSTM, a specialized form of recurrent neural network (RNN), is particularly well-suited for handling time series data due to its ability to capture long-term dependencies and patterns. The research aims to forecast future SO2 concentrations based on historical observations, a critical task for air quality management. Various model configurations were evaluated, with a specific focus on determining the optimal time lag for accurate predictions. The study found that a lag of seven time steps yielded the best results, achieving a Root Mean Squared Error (RMSE) of 2.588900. These findings highlight the model's capability for precise forecasting, making it a valuable tool for real-time monitoring and pollution control. Furthermore, the approach is adaptable and can be extended to predict other environmental pollutants, thus contributing to broader public health and environmental protection efforts. This work underscores the potential of deep learning methodologies in tackling air pollution challenges and mitigating its adverse effects on both human health and the ecosystem.