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:: Volume 12, Issue 2 (1-2023) ::
JGST 2023, 12(2): 136-151 Back to browse issues page
Spatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms
Amin Gholami , Yasser Ebrahimian Ghajari *
Abstract:   (1079 Views)
PM 2.5 (particles <2.5 μm in aerodynamic diameter) can be measured by ground station data in urban areas, but the number of these stations and their geographical coverage is limited. Therefore, these data are not adequate for calculating concentrations of Pm2.5 over a large urban area. This study aims to use Aerosol Optical Depth (AOD) satellite images and meteorological data from 2014 to 2017 for spatial distribution simulation of PM 2.5 concentrations over the mega-city of Tehran. The Multilayer Perceptron (MLP), Multiple Linear Regression (MLR), and Decision Tree (DT) models were used to estimate the concentrations of PM 2.5. The results showed that MLP with a root mean square error (RMSE) of 11.46 and R2 coefficient of 0.67 outperformed the MLR and DT models. However, the best model had low prediction accuracy. So, three optimization algorithms, namely, particle swarm optimization (PSO), Genetic Algorithm (GA), and Migration-Based Genetic Algorithm (MBGA) were used to improve the accuracy of the models. The use of GA and MBGA algorithms improved the accuracy of the models significantly and led to the RMSE of 1.71 and R2 of 0.99 for the hybrid model of MBGA-MLP. The proposed hybrid models in this paper can be used to estimate the PM2.5 concentrations.
 
Article number: 10
Keywords: Air Pollution, Aerosol Optical Depth, Multilayer Perceptron, Migration-Based Genetic Algorithm, PM 2.5, Tehran.
Full-Text [PDF 782 kb]   (652 Downloads)    
Type of Study: Research | Subject: GIS
Received: 2022/08/12
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Gholami A, Ebrahimian Ghajari Y. Spatiotemporal Estimation of PM2.5 Concentration Using Remotely Sensed Data, Machine Learning, and Optimization Algorithms. JGST 2023; 12 (2) : 10
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Volume 12, Issue 2 (1-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology