PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data
Chinese Academy of Sciences · Institute of Remote Sensing and Digital Earth · +2 more institutions
Abstract
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 (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML)…
Citation impact
- FWCI
- 22.79
- Percentile
- 100%
- References
- 57
Authors
5- MZMehdi Zamani
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science
- CCChunxiang CaoCorresponding
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science
- XNXiliang Ni
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science
- BBBarjeece Bashir
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science
- STSomayeh Talebiesfandarani
Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, University of Chinese Academy of Sciences, State Key Laboratory of Remote Sensing Science
Topics & keywords
- Random forest
- Environmental science
- Gradient boosting
- Particulates
- Predictive modelling
- Satellite
- Aerosol
- Meteorology
- Sustainable cities and communities