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• W4379382968 abstract "The southeastern section of Iran, especially the province of Khuzestan, experience severe air pollution levels, such as high values of surface dust mass concentration (SDMC). The province lacks accurate and well-placed ground observational stations, therefore the only viable approach for evaluating SDMC is via remote sensing. In this study, meteorological, hydrological and geological data on 11 input variables from Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2), global precipitation measurement (GPM) and Global Land Data Assimilation System (GLDAS) for the year 2018 are used for prediction of the SDMC values, also obtained from another MERRA-2 mission. For real-time prediction, Pearson’s correlation coefficient (PCC) analysis shows that wind-related variables—surface wind speed, surface aerodynamic conductance and surface pressure—are those with the highest correlation with SDMC. Using the gradient boosting regression (GBR) algorithm, these three variables can simulate SDMC with good accuracy $$(R^{2} = 0.76,;NSE = 0.76, ;N{text{-}}RMSE = 0.48 ;and;N{text{-}}MAE = 0.34)$$ . Furthermore, near-future SDMC forecasting down to 8 days prior of SMDC occurrence is also carried out. A sequential forward feature selection of the input variables, based on PCC, is used for four lead times and results show that surface pressure and heat flux govern near-future predictions. With $$R^{2} = 0.46$$ and $$N{text{-}}RMSE = 0.74$$ , GBR shows good potential for forecasting SDMC 8 days in advance. Real-time and near-future simulation results generally show that robust SDMC prediction can be obtained using exclusively remote sensing data, without ground-based observations." @default.
• W4379382968 created "2023-06-06" @default.
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• W4379382968 date "2023-06-05" @default.
• W4379382968 modified "2023-10-10" @default.
• W4379382968 title "Satellite-based prediction of surface dust mass concentration in southeastern Iran using an intelligent approach" @default.
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• W4379382968 doi "https://doi.org/10.1007/s00477-023-02473-6" @default.
• W4379382968 hasPublicationYear "2023" @default.
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