Jing Deng
Research Subjects
Research Interests
Jing Deng has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Liu Yang
School of Information Engineering, Jiangxi University of Water Resources and Electric Power, Nanchang 330099, China
1 shared publication - Shaoquan Zhang
Nanchang Institute of Technology
1 shared publication - Xinyi Cui
School of Information Engineering, Jiangxi University of Water Resources and Electric Power, Nanchang 330099, China
1 shared publication - Yeheng Liang
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
1 shared publication - Yuming Tang
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
1 shared publication - Zuhan Liu
School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
1 shared publication
Latest Publications
- Open Access
- 22 November 2025
- Published by MDPI AG in Atmosphere
- Vol. 16 (12) , 1317
High-Resolution Spatial Prediction of Daily Average PM2.5 Concentrations in Jiangxi Province via a Hybrid Model Integrating Random Forest and XGBoost
Numerous machine learning models have been widely used for the spatial prediction of PM2.5 mass concentrations in the field of remote sensing, but most studies rely on single models, limiting their ability to capture complex nonlinear relationships. Furthermore, traditional Aerosol Optical Depth (AOD) methods suffer from extensive missing values due to algorithmic limitations, hindering daily PM2.5 mass concentration retrieval. This study first developed a hybrid random forest and extreme gradient boosting model (RF-XGBoost) to overcome single-model accuracy constraints. Subsequently, Top-of-Atmosphere (TOA) reflectance replaced conventional AOD as the hybrid model’s input. Finally, we integrated four-year (2020–2023) TOA reflectance, normalized difference vegetation index (NDVI) data, meteorological data, digital elevation model (DEM) data, and day-of-year data to develop a high-precision hybrid model specifically optimized for Jiangxi Province. The simulation results demonstrated that the hybrid RF-XGBoost model (test-R2 = 0.82, RMSE = 7.25 μg/m3, MAE = 4.90 μg/m3) outperformed the single Random Forest Model by 25% and 26% in terms of the root mean square error (RMSE) and mean absolute error (MAE), respectively. The high predictive accuracy of our method confirms its effectiveness in generating reliable PM2.5 estimates. The resulting four-year dataset also successfully delineated the characteristic seasonal PM2.5 pattern in the region, with the highest levels in winter and the lowest in summer, alongside a clear decreasing annual trend, signifying gradual atmospheric improvement.
- 22 November 2025
- Published by MDPI AG in Atmosphere
- Vol. 16 (12) , 1317
Open AccessHigh-Resolution Spatial Prediction of Daily Average PM2.5 Concentrations in Jiangxi Province via a Hybrid Model Integrating Random Forest and XGBoost
Numerous machine learning models have been widely used for the spatial prediction of PM2.5 mass concentrations in the field of remote sensing, but most studies rely on single models, limiting their ability to capture complex nonlinear relationships. Furthermore, traditional Aerosol Optical Depth (AOD) methods suffer from extensive missing values due to algorithmic limitations, hindering daily PM2.5 mass concentration retrieval. This study first developed a hybrid random forest and extreme gradient boosting model (RF-XGBoost) to overcome single-model accuracy constraints. Subsequently, Top-of-Atmosphere (TOA) reflectance replaced conventional AOD as the hybrid model’s input. Finally, we integrated four-year (2020–2023) TOA reflectance, normalized difference vegetation index (NDVI) data, meteorological data, digital elevation model (DEM) data, and day-of-year data to develop a high-precision hybrid model specifically optimized for Jiangxi Province. The simulation results demonstrated that the hybrid RF-XGBoost model (test-R2 = 0.82, RMSE = 7.25 μg/m3, MAE = 4.90 μg/m3) outperformed the single Random Forest Model by 25% and 26% in terms of the root mean square error (RMSE) and mean absolute error (MAE), respectively. The high predictive accuracy of our method confirms its effectiveness in generating reliable PM2.5 estimates. The resulting four-year dataset also successfully delineated the characteristic seasonal PM2.5 pattern in the region, with the highest levels in winter and the lowest in summer, alongside a clear decreasing annual trend, signifying gradual atmospheric improvement.