Air Pollution Monitoring using Online Recurrent Extreme Learning Machine

Air pollution, particularly high concentrations of Ozone (O3), poses a serious threat to human health and the environment. While deep learning algorithms have proven effective in air quality forecasting, current offline models struggle to capture the dynamic, time-evolving patterns generated by continuous air pollution monitoring data. Further, the time-consuming training process and computational demands hinder the practicality of these models. This paper presents a lightweight incremental learning model tailored for O3 forecasting. To evaluate its effectiveness, real data is employed and performance is evaluated using forecasting metrics and computational time. The results reveal that the incremental learning model surpasses the state-of-the-art model widely used in O3 and time series forecasting, demonstrating both superior accuracy and computational efficiency.