Fusion of statistical and machine learning approaches for time series prediction using earth observation data

Ehsani

et al. 2023

Water Air Soil Pollut

Section: Methodsmentioning

confidence: 99%

Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

Ehsani

et al. 2023

Water Air Soil Pollut

“…The values of the p and q variables can be found by one of the functions: autocorrelation function (ACF), or partial autocorrelation function (PACF). We used both of these functions here to find the most suitable order (Agrawal et al 2017 ). To establish whether an ARIMA model qualifies to predict the concentration of sulfur dioxide, PM 2.5 and PM 10 and wind Speed and relative humidity parameters in Tehran, statistical errors through determination coefficient (R 2 ), mean square error (MSE) and root mean square error (RMSE) were applied as follows (Beckerman et al 2013 ; Elavarasan et al 2018 ; Goap et al 2018 ): where and are the forecasted and observed values of , and represent the mean values of the forecasted and observed in the tested sample set and denotes the number of datum points in the set.…”

Section: Methodsmentioning

confidence: 99%

Estimation of short-lived climate forced sulfur dioxide in Tehran, Iran, using machine learning analysis

Stoch Environ Res Risk Assess

Rashidi

et al. 2022

This paper presents a time-series analysis of SO 2 air concentration and the effects of particulates (either PM 2.5 and PM 10 ) concentrations and meteorological conditions (relative humidity and wind speed) on SO 2 trend in Tehran for the period from 2011 to 2020. The source data were obtained from 21 monitoring stations of Air Quality Control Company and meteorological stations in Tehran. To predict the status of future concentration of SO 2 , PM 2.5 and PM 10 , a Box–Jenkins ARIMA approach was used to model the monthly time series. Considering the whole period of ten years, a somewhat downward trend was noted for SO 2 air concentration, even though a slight rising trend was observed in 2020 year. Monthly sulfur dioxide concentrations showed the lowest value in June and the highest value in January. Seasonal concentrations were lowest in spring and highest in winter. Then, in the ArcGIS software, the IDW method was used to obtain air pollution zoning maps. As a result, the highest average concentration of SO 2 occurred in the north and southwest of Tehran. In the last step, Relations between the SO 2 concentration and particulate matters and relative humidity and wind speed were calculated statistically using the daily average data. We finally concluded that the combined effect of particulate matters and relative humidity with the increasing role of Sulfur dioxide overcomes the decreasing role of wind speed. This study can contribute to a better understanding of the SO 2 air pollution in Tehran affected by meteorological conditions and the rapid urbanization and industrialization, followed by the possible combustion of fuel oil in power plants and health problems.

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On the predictability of short-lived particulate matter around a cement plant in Kerman, Iran: machine learning analysis

Int. J. Environ. Sci. Technol.

Ehsani

et al. 2022