Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from
  MODIS MAIAC AOD in Tehran, Iran
Hossein Bagheri
High resolution mapping of PM2.5 concentration over Tehran city is
challenging because of the complicated behavior of numerous sources of
pollution and the insufficient number of ground air quality monitoring
stations. Alternatively, high resolution satellite Aerosol Optical Depth (AOD)
data can be employed for high resolution mapping of PM2.5. For this purpose,
different data-driven methods have been used in the literature. Recently, deep
learning methods have demonstrated their ability to estimate PM2.5 from AOD
data. However, these methods have several weaknesses in solving the problem of
estimating PM2.5 from satellite AOD data. In this paper, the potential of the
deep ensemble forest method for estimating the PM2.5 concentration from AOD
data was evaluated. The results showed that the deep ensemble forest method
with R2 = 0.74 gives a higher accuracy of PM2.5 estimation than deep learning
methods (R2 = 0.67) as well as classic data-driven methods such as random
forest (R2 = 0.68). Additionally, the estimated values of PM2.5 using the deep
ensemble forest algorithm were used along with ground data to generate a high
resolution map of PM2.5. Evaluation of the produced PM2.5 map revealed the good
performance of the deep ensemble forest for modeling the variation of PM2.5 in
the city of Tehran.