PM_2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data

• Main Authors: Mehdi Zamani Joharestani, Chunxiang Cao, Xiliang Ni, Barjeece Bashir, Somayeh Talebiesfandarani
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Atmosphere
• Subjects: PM_2.5; prediction; XGBoost; random forest; deep leaning; feature importance
• Online Access: https://www.mdpi.com/2073-4433/10/7/373

In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM_2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM_2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM_2.5 concentrations, the factors influencing PM_2.5 prediction have not been investigated. In this work, we study feature importance for PM_2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM_2.5, and geographical data, in the modeling. The best model performance obtained was R² = 0.81 (R = 0.9), MAE = 9.93 µg/m³, and RMSE = 13.58 µg/m³ using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R² varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM_2.5 lag data, satellite-derived AODs did not improve model performance.