Particulate Matter Prediction and Shapley Value Interpretation in Korea through a Deep Learning Model

Le résumé fourni par la source

This study collected and analyzed data to predict particulate matter (PM) concentrations in Korea at regular intervals. Automated synoptic observation system data, real-time atmospheric observation data from AirKorea, and Geostationary Korea Multipurpose Satellite – 2A data were used. We also used deep learning, which is useful for PM predictions. The deep learning model used a neural network (NN) to predict concentrations of PM with a diameter less than 2.5 μm (PM2.5) and PM with a diameter less than 10 μm (PM10). To illustrate the results of the NN model, we calculated the Shapley value using eXplanable Artificial Intelligence (XAI) in the SHapley Additive exPlanations (SHAP) library. The difference in the analysis according to the diameter of aerosols was explained. To analyze the contribution of features for each grid, the SHAP values were normalized. The normalized SHAP values were clustered and represented visually. PM2.5 and PM10 were classified into four clusters. The next day's PM2.5 and PM10 predictions were both heavily influenced by weather variables in the western region, and air quality data were more influential in the inland region. Unlike PM2.5, the next day's PM10 prediction in the southern region was affected to a greater degree by the wind.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.