ABSTRACT
After February 2020, the majority of the world's governments decided to implement a lockdown in order to limit the spread of the deadly COVID-19 virus. This restriction improved air quality by reducing emissions of particular atmospheric pollutants from industrial and vehicular traffic. In this study, we look at how the COVID-19 shutdown influenced the air quality in Lahore, Pakistan. HAC Agri Limited, Dawn Food Head Office, Phase 8-DHA, and Zeenat Block in Lahore were chosen to give historical data on the concentrations of many pollutants, including PM2.5, PM10 (particulate matter), NO2 (nitrogen dioxide), and O3 (ozone). We use a variety of models, including decision tree, SVR, random forest, ARIMA, CNN, N-BEATS, and LSTM, to compare and forecast air quality. Using machine learning methods, we looked at how each pollutant's levels changed during the lockdown. It has been shown that LSTM estimates the amounts of each pollutant during the lockout more precisely than other models. The results show that during the lockdown, the concentration of atmospheric pollutants decreased, and the air quality index improved by around 20%. The results also show a 42% drop in PM2.5 concentration, a 72% drop in PM10 concentration, a 29% drop in NO2 concentration, and an increase of 20% in O3 concentration. The machine learning models are assessed using the RMSE, MAE, and R-SQUARE values. The LSTM measures NO2 at 4.35%, O3 at 8.2%, PM2.5 at 4.46%, and PM10 at 8.58% in terms of MAE. It is observed that the LSTM model outperformed with the fewest errors when the projected values are compared with the actual values.
ABSTRACT
Coronavirus disease 2019 (COVID-19) virus was first seen in 2019 December in China and rapidly spread all over the world and millions of people are infected with this virus. This disease has sited the entire world in dangerous circumstances. At the start of this virus, it was a very serious matter in China but now it is being observed all over the world. The virus is life-threatening, and other public who are affected by previous diseases or those people whose age is more than 60 are more affected by this virus. The healthcare and drug industries have tried to find a treatment. While machine learning algorithms are largely applied in other areas, at this time every health care unit has to want to use machine learning techniques to find, predict, track, and screen the spread of COVID-19, and try to find the treatment of it. we show what is the journey of machine learning to find and track COVID-19 and also observing it from a screening and detecting the COVID-19. We show how much research has been done yet to detection of COVID-19 and which algorithm of machine learning is best for the detection and screening of the COVID-19.