Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran
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arXiv
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916114474205184 |
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| author | Bagheri, Hossein |
| author_facet | Bagheri, Hossein |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02139 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran Bagheri, Hossein Machine Learning 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. |
| title | Using Deep Ensemble Forest for High Resolution Mapping of PM2.5 from MODIS MAIAC AOD in Tehran, Iran |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.02139 |