Journal of Agricultural Meteorology

Journal of Agricultural Meteorology

Modeling and forecasting dust pollution using satellite data and machine learning techniques (A case study of Ahvaz)

Document Type : Original Article

Authors
Alireza Yousefi kebriya 1
mehdi nadi 2
Ebadat Ghanbariparmehr 3
Sun Zhongchang 4
1 1- Ph.D. Candidate, Department of Water Engineering and Agricultural Meteorology, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran.
2 Water engineering dept. Faculty of agricultural engineering , Sari agricultural sciences and natural resources university
3 3- Assistant Professor, Department of Remote Sensing, Faculty of Surveying Engineering, Noshirvani Babol University of Technology, Babol, Iran.
4 International Research Center of Big Data for Sustainable Development Goals, 100094, Beijing, China
10.22125/agmj.2024.478677.1174
Abstract
Dust pollution presents a significant environmental challenge in arid and semi-arid regions, adversely impacting public health, agriculture, water resources, and infrastructure. This study aimed to forecast PM10 dust pollution in Ahvaz by utilizing MODIS satellite AOD data collected from 2016 to 2022, alongside four machine learning techniques: XGBoost, SGB, LSTM, and MLP. Initially, the HYSPLIT model was employed to track dust paths and analyze their frequency. Subsequently, true color satellite images were used to identify virtual stations along these dust paths, and the AOD values at these locations served as input data for the forecasting models. The results indicated a significant correlation between the satellite AOD data and actual PM10 data for 2021 and 2022, at significance level of 0.01 and determination coefficients of 0.89 and 0.85, respectively. Dust tracking maps demonstrated that dust was being transported from Iraq to Ahvaz. Average AOD data from eight virtual stations, with a one-day lag, was used for weekly dust prediction. Model evaluation results revealed that XGBoost outperformed the other models, achieving a mean absolute error of 0.08 and a coefficient of determination of 0.87, followed by the SGB and LSTM models with almost same performance, while the MLP model showed the lowest accuracy. Predictions for the following seven days indicated that boosting models, particularly XGBoost, effectively captured data fluctuations. This research demonstrates that AOD satellite data, combined with advanced machine learning techniques, are valuable tools for forecasting and managing dust pollution.
Keywords
AOD index
Dust tracking
HYSPLIT model
XGBoost
Subjects
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