Explainable deep learning on multi-target time series forecasting: An air pollution use case

Abstract

Urban air pollution represents a significant threat to public health and the environment, with nitrogen oxides, ozone, and particulate matter being among the most harmful pollutants. These contribute to respiratory and cardiovascular diseases, particularly in urban areas with high traffic and elevated temperatures. Machine learning, especially deep learning, shows promise in enhancing the prediction accuracy of prediction of pollutant's concentrations. However, the “black box” nature of these models often limits their interpretability, which is crucial for informed decision-making. Our study introduces a Temporal Selection Layer technique within deep learning models for time series forecasting to tackle this issue. This technique not only improves prediction accuracy by embedding feature selection directly into the neural network, but also enhances interpretability and reduces computational costs. In particular, we applied this method to hourly concentration data of pollutants, including particulate matter, ozone, and nitrogen oxides, from five urban monitoring sites in Graz, Austria. These concentrations were used as target variables to predict, while identifying the most relevant features and periods that affect prediction accuracy. Comparative analysis with other embedded feature selection methods showed that the Temporal Selection Layer significantly enhances both model effectiveness and transparency. Additionally, we applied explainable techniques to evaluate the impact of weather and time-related factors on air pollution, which also helped assess feature importance. The results show that our approach improves both prediction accuracy and model interpretability, leading finally to more effective pollution management strategies. • Major pollutants include nitrogen oxides, ozone, and particulate matter, which contribute to respiratory and cardiovascular diseases, especially in high-traffic and hot urban areas. • Digital platforms use real-time sensor and station data to track pollution but struggle with accurate future predictions. • Deep learning shows potential in improving pollutant prediction accuracy but suffers from interpretability issues. • The study introduces the ”Temporal Selection Layer” for deep learning models, which integrates feature selection into the network, enhancing prediction accuracy, interpretability, and reducing computational costs. • This technique was applied to hourly pollutant data from five urban sites in Graz, Austria, improving model effectiveness and transparency compared to other methods. • Explainable techniques were used to assess the impact of weather and time factors on pollution, supporting better pollution management strategies. • The approach improves prediction accuracy and model interpretability, leading to more effective air quality control.

Topics & Concepts