Authors :
Rafay Malik; Muhammad Mudassir; Hasnain Aslam
Volume/Issue :
Volume 7 - 2022, Issue 7 - July
Google Scholar :
https://bit.ly/3IIfn9N
Scribd :
https://bit.ly/3OQjuUl
DOI :
https://doi.org/10.5281/zenodo.6957827
Abstract :
At present time, the forecasting of air particles
has been a current research topic due to increase in bad
air quality because of industrialization and increase in
pollution due to vehicle and in COVID-19 when there was
lockdown it came in our notice that this thing can be
controlled its effects which was highlighted due to COVID
19 lockdown period. It includes a various source of
contamination, making it hard to decide the entirety of the
contributing meteorological and ecological components. At
the point when just the PM 2.5 fixationtime sequences are
taken without other external data, precise estimating is
significant and productive. For address, this issue this paper
presents the ARIMA based model for forecast PM 2.5 data
concerning time. In this paper, two methods are proposed.
First dividing the data (80 % training) and (20 % testing)
then decomposes one-dimensional data through wavelet
decompositionof level-2 dB2. Then, it uses ARIMA model
method to forecast each divided sequel and reconstruct its
predicted results to obtainthe finalize predicting outcomes.
Second without decomposes the data, we directly apply the
ARIMA model and forecast the results. The ARIMA model
has forecast more accurate results concerning predicting
the concentration of PM 2.5 as compare to the
WAVELET-ARIMA model. The two proposed ARIMA
and Wavelet ARIMA can be efficiently applied to
forecastingPM 2.5 concentration in short term and can be
enhancing the accuracy. Moreover, relating the forecasted
results with the policygoverning to control the pollution as
shown by implementinglockdown as PM 2.5 value has been
reduced up to 50% in different cities during the lock down
period which can be seen from study.
Keywords :
Particulate Matter 2.5, Auto Regressive Integrated Moving Average, Mean Absolute Value, Weather Research and Forecasting, Carbon Matter, Elemental Carbon, Root Mean Square Error.
At present time, the forecasting of air particles
has been a current research topic due to increase in bad
air quality because of industrialization and increase in
pollution due to vehicle and in COVID-19 when there was
lockdown it came in our notice that this thing can be
controlled its effects which was highlighted due to COVID
19 lockdown period. It includes a various source of
contamination, making it hard to decide the entirety of the
contributing meteorological and ecological components. At
the point when just the PM 2.5 fixationtime sequences are
taken without other external data, precise estimating is
significant and productive. For address, this issue this paper
presents the ARIMA based model for forecast PM 2.5 data
concerning time. In this paper, two methods are proposed.
First dividing the data (80 % training) and (20 % testing)
then decomposes one-dimensional data through wavelet
decompositionof level-2 dB2. Then, it uses ARIMA model
method to forecast each divided sequel and reconstruct its
predicted results to obtainthe finalize predicting outcomes.
Second without decomposes the data, we directly apply the
ARIMA model and forecast the results. The ARIMA model
has forecast more accurate results concerning predicting
the concentration of PM 2.5 as compare to the
WAVELET-ARIMA model. The two proposed ARIMA
and Wavelet ARIMA can be efficiently applied to
forecastingPM 2.5 concentration in short term and can be
enhancing the accuracy. Moreover, relating the forecasted
results with the policygoverning to control the pollution as
shown by implementinglockdown as PM 2.5 value has been
reduced up to 50% in different cities during the lock down
period which can be seen from study.
Keywords :
Particulate Matter 2.5, Auto Regressive Integrated Moving Average, Mean Absolute Value, Weather Research and Forecasting, Carbon Matter, Elemental Carbon, Root Mean Square Error.
