Selected article for: "lockdown period and long lstm short term memory"
Author: Alvi, A.; Ahmed, M.; Hoque, S. N. M. A.
Title: Consequences of lockdown caused by COVID-19 outbreak on the quality of air in Dhaka
  • Cord-id: 36436d9i
  • Document date: 2021_1_1
    • ID: 36436d9i
    Snippet: Air pollution has become a worldwide problem that has a negative impact on both human health and the environment. The development of systems for predicting air pollutant severities ahead of time is being driven by this rising threat. In this paper, we proposed using a Long short-term memory (LSTM) model of an Artificial Neural Network (ANN) to predict air pollutant severity levels, as a time series during the COVID-19 lockdown period, providing an early warning. The research used three types of
    Document: Air pollution has become a worldwide problem that has a negative impact on both human health and the environment. The development of systems for predicting air pollutant severities ahead of time is being driven by this rising threat. In this paper, we proposed using a Long short-term memory (LSTM) model of an Artificial Neural Network (ANN) to predict air pollutant severity levels, as a time series during the COVID-19 lockdown period, providing an early warning. The research used three types of real time datasets of Dhaka city that included records of three gaseous pollutants (CO, NO2, PM2.5). Modeling of the dataset of each pollutant was carried out on hourly and minute-based intervals in two different locations, Mirpur and Baridhara. The predicted results were compared with the readings of the dataset and the model attained high accuracy in predicting air quality. Finally, the air pollutants data were analyzed with COVID 19 cases. Our analysis reviews that the concentrations of air pollutants are in agreement with the regional COVID 19 cases. © 2021 IEEE.

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