Nationwide Machine Learning-Ensemble PM_2.5 Mapping Prediction and Forecasting Models in South Korea with High Spatiotemporal Resolution and Health Risk Estimation-Based Evaluations.

ABSTRACT: Several studies developed machine learning-based PM2.5 prediction models; however, nationwide models addressing both mapping prediction and forecasting were limited. Further, although the prediction accuracy is different from PM2.5-related health risk estimation, previous studies solely examined the prediction accuracy. This study suggests a method to assess the statistical properties of PM2.5-health risk estimation, which also can be used as a model selection. We used three machine learning algorithms and an ensemble method to construct PM2.5 mapping prediction (1 km2) and two-day forecasting models majorly using satellite-driven data in South Korea (2015-2022). We performed a simulation study to examine the statistical properties of short-term PM2.5 risk estimation using prediction model

SUBMITTER: Ahn S 

PROVIDER: S-EPMC12362205 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature