Forecasting Hazard Level of Air Pollutants Using LSTM’s - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2020

Forecasting Hazard Level of Air Pollutants Using LSTM’s

Abstract

The South Asian countries have the most polluted cities in the world which has caused quite a concern in the recent years due to the detrimental effect it had on economy and on health of humans and crops. PM 2.5 in particular has been linked to cardiovascular diseases, pulmonary diseases, increased risk of lung cancer and acute respiratory infections. Higher concentration of surface ozone has been observed to have negatively impacted agricultural yield of crops. Due to its deleterious impact on human health and agriculture, air pollution cannot be brushed off as a trivial matter and measures must be taken to address the problem. Deterministic models have been actively used; but they fall short due to their complexity and inability to accurately model the problem. Deep learning models have however shown potential when it comes to modeling time series data. This article explores the use of recurrent neural networks as a framework for predicting the hazard levels in Lahore, Pakistan with 95.0% accuracy and Beijing, China with 98.95% using the time series data of air pollutants and meteorological parameters. Forecasting air quality index (AQI) and Hazard levels would help the government take appropriate steps to enact policies to reduce the pollutants and keep the citizens informed about the statistics.
Fichier principal
Vignette du fichier
500087_1_En_13_Chapter.pdf (723.92 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04060653 , version 1 (06-04-2023)

Licence

Attribution

Identifiers

Cite

Saba Gul, Gul Muhammad Khan. Forecasting Hazard Level of Air Pollutants Using LSTM’s. 16th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2020, Neos Marmaras, Greece. pp.143-153, ⟨10.1007/978-3-030-49186-4_13⟩. ⟨hal-04060653⟩
9 View
2 Download

Altmetric

Share

Gmail Facebook X LinkedIn More