An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique

Almalawi, A. and Alsolami, F. and Khan, A.I. and Alkhathlan, A. and Fahad, A. and Irshad, K. and Qaiyum, S. and Alfakeeh, A.S. (2022) An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique. Environmental Research, 206.

Full text not available from this repository.
Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Air pollution is the existence of atmospheric chemicals damaging the health of human beings and other living organisms or damaging the environment or resources. Rarely any common contaminants are smog, nicotine, mold, yeast, biogas, or carbon dioxide. The paper will primarily observe, visualize and anticipate pollution levels. In particular, three algorithms of Artificial Intelligence were used to create good forecasting models and a predictive AQI model for 4 distinct gases: carbon dioxide, sulphur dioxide, nitrogen dioxide, and atmospheric particulate matter. Thus, in this paper, the Air Qualification Index is developed utilizing Linear Regression, Support Vector Regression, and the Gradient Boosted Decision Tree GBDT Ensembles model over the next 5 h and analyzes air qualities using various sensors. The hypothesized artificial intelligence models are evaluated to the Root Mean Squares Error, Mean Squared Error and Mean absolute error, depending upon the performance measurements and a lower error value model is chosen. Based on the algorithm of the Artificial Intelligent System, the level of 5 air pollutants like CO2, SO2, NO2, PM 2.5 and PM10 can be predicted immediately by integrating the observations with errors. It may be used to detect air quality from distance in large cities and can assist lower the degree of environmental pollution. © 2021 Elsevier Inc.

Item Type: Article
Impact Factor: cited By 0
Uncontrolled Keywords: carbon dioxide; nitrogen dioxide; sulfur dioxide; nitrogen dioxide, artificial intelligence; atmospheric pollution; nitrogen dioxide; particulate matter; pollution monitoring; sulfur dioxide, air monitoring; air pollution; air quality control; ambient air; Article; artificial intelligence; controlled study; decision tree; environmental exposure; environmental impact; forecasting; internet of things; learning algorithm; particulate matter 10; particulate matter 2.5; particulate matter exposure; performance indicator; prognosis; support vector machine; air pollutant; artificial intelligence; environmental monitoring; human; particulate matter, Air Pollutants; Air Pollution; Artificial Intelligence; Environmental Monitoring; Humans; Nitrogen Dioxide; Particulate Matter
Depositing User: Ms Sharifah Fahimah Saiyed Yeop
Date Deposited: 06 Jul 2022 07:58
Last Modified: 06 Jul 2022 07:58
URI: http://scholars.utp.edu.my/id/eprint/33142

Actions (login required)

View Item
View Item