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

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...

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Main Authors: Almalawi, A., Alsolami, F., Khan, A.I., Alkhathlan, A., Fahad, A., Irshad, K., Qaiyum, S., Alfakeeh, A.S.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.33142 /
Published: Academic Press Inc. 2022
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122462108&doi=10.1016%2fj.envres.2021.112576&partnerID=40&md5=80cf915ff4c9ab9a7bfff75a72b99ad1
http://eprints.utp.edu.my/33142/
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spelling utp-eprints.331422022-07-06T07:58:47Z An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique Almalawi, A. Alsolami, F. Khan, A.I. Alkhathlan, A. Fahad, A. Irshad, K. Qaiyum, S. Alfakeeh, A.S. 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. Academic Press Inc. 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122462108&doi=10.1016%2fj.envres.2021.112576&partnerID=40&md5=80cf915ff4c9ab9a7bfff75a72b99ad1 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 . http://eprints.utp.edu.my/33142/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Almalawi, A.
Alsolami, F.
Khan, A.I.
Alkhathlan, A.
Fahad, A.
Irshad, K.
Qaiyum, S.
Alfakeeh, A.S.
spellingShingle Almalawi, A.
Alsolami, F.
Khan, A.I.
Alkhathlan, A.
Fahad, A.
Irshad, K.
Qaiyum, S.
Alfakeeh, A.S.
An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
author_sort Almalawi, A.
title An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
title_short An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
title_full An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
title_fullStr An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
title_full_unstemmed An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
title_sort iot based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique
publisher Academic Press Inc.
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122462108&doi=10.1016%2fj.envres.2021.112576&partnerID=40&md5=80cf915ff4c9ab9a7bfff75a72b99ad1
http://eprints.utp.edu.my/33142/
_version_ 1741197809105764352
score 11.62408

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