Document Type : Research Paper
Authors
1
Urban Planning Expert mollasani iran.
2
Department of Soil Sciences, Faculty of Agriculture, University of Khuzestan Agricultural Sciences and Natural Resources, Mollasani, Iran.
3
Department of Soil Science, Shahid Chamran University of Ahvaz, Ahvaz, Khuzestan, Iran
4
Department of Software Engineering, Islamic Azad University, Ahvaz Branch, Khuzestan, Iran
Abstract
Monitoring and controlling the level and sources of dust are crucial in the face of climate change and the development of suitable predictive approaches that directly impact the environment and human health. This study aims to estimate the concentration of PM10 in the city of Ahvaz using various machine learning models. Climate variables and the Aerosol Optical Depth (AOD) index, derived from the MODIS sensor at a wavelength of 476 nanometers, were used as influential variables in estimating PM10 concentration in three scenarios: combining AOD with PM10 (scenario 1), combining climate variables with PM10 (scenario 2), and combining climate variables and AOD with PM10 (scenario 3) .Using six machine learning algorithms, namely Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Artificial Neural Networks (ANN), AdaBoostR with DTR, Support Vector Regression (SVR), and Decision Tree Regression (DTR), the PM10 concentration was estimated in different scenarios, considering accuracy and precision coefficients. The most influential variables in estimating PM10 concentration were determined to be sunshine hours, minimum visibility, maximum wind speed, and the AOD index. The GBR linear regression model, with R2, MAE, RMSE, and IOA coefficients of 0.76, 0.31, 0.49 and 0.93 respectively, was found to be the most suitable model for estimating PM10 concentration in scenario 3. the results showed that incorporating the AOD index alongside climate variables improved the model's performance in estimating PM10 concentration. The proposed final model can be used for daily estimation of PM10 particles.
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