Several studies developed machine learning-based PM_(2.5) prediction models;however,nationwide models addressing both mapping prediction and forecasting were limited.Further,although the prediction accuracy is differe...Several studies developed machine learning-based PM_(2.5) prediction models;however,nationwide models addressing both mapping prediction and forecasting were limited.Further,although the prediction accuracy is different from PM_(2.5)-related health risk estimation,previous studies solely examined the prediction accuracy.This study suggests a method to assess the statistical properties of PM_(2.5)-health risk estimation,which also can be used as a model selection.We used three machine learning algorithms and an ensemble method to construct PM_(2.5) mapping prediction(1 km^(2))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 PM_(2.5) risk estimation using prediction models.Our ensemble spatial prediction model showed better performance than single algorithms(0.956 test R^(2)).The range of the R^(2) values was 0.78−0.98 across the monitoring sites.The average%bias was from 1.403%−1.787%when our mapping models for PM_(2.5)-mortality risk estimation,compared to the estimates from monitored PM_(2.5).The best R^(2) of our forecasting models was 0.904.This study developed machine learning models for spatial PM_(2.5) predictions and forecasting in Korea.This study also suggested a method to address risk estimation and model selection concurrently when multiple prediction models were used.展开更多
基金supported by the National Institute of Environmental Research(NIER)funded by the Ministry of Environment(MOE)of the Republic of Korea(NIER-2021-03-03-007)supported by Institute of Information&communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2025-RS-2023-00254177)grant funded by the Korean government(MIST)supported by the Korea Environment Industry&Technology Institute(KEITI)through“Climate Change R&D Project for New Climate Regime.”,funded by Korea Ministry of Environment(MOE)(RS-2022-KE002235).
文摘Several studies developed machine learning-based PM_(2.5) prediction models;however,nationwide models addressing both mapping prediction and forecasting were limited.Further,although the prediction accuracy is different from PM_(2.5)-related health risk estimation,previous studies solely examined the prediction accuracy.This study suggests a method to assess the statistical properties of PM_(2.5)-health risk estimation,which also can be used as a model selection.We used three machine learning algorithms and an ensemble method to construct PM_(2.5) mapping prediction(1 km^(2))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 PM_(2.5) risk estimation using prediction models.Our ensemble spatial prediction model showed better performance than single algorithms(0.956 test R^(2)).The range of the R^(2) values was 0.78−0.98 across the monitoring sites.The average%bias was from 1.403%−1.787%when our mapping models for PM_(2.5)-mortality risk estimation,compared to the estimates from monitored PM_(2.5).The best R^(2) of our forecasting models was 0.904.This study developed machine learning models for spatial PM_(2.5) predictions and forecasting in Korea.This study also suggested a method to address risk estimation and model selection concurrently when multiple prediction models were used.
