NDLI: Prediction of Ambient Air Quality Based on Neural Network Technique

Content Provider	IEEE Xplore Digital Library
Author	Kamal, M.M. Jailani, R. Shauri, R.L.A.
Copyright Year	2006
Description	Author affiliation: Fac. of Electr. Eng., Univ. Teknol. MARA, Shah Alam (Kamal, M.M.; Jailani, R.; Shauri, R.L.A.)
Abstract	Air quality index (AQI) system lays an important role in conveying to both decision-makers and the general public the status of ambient air quality, ranging from good to hazardous. Five types of air pollutants will be studied which consists of ozone $(O_{3}),$ carbon monoxide (CO), nitrogen dioxide $(NO_{2}),$ sulphur dioxide $(SO_{2})$ and suspended particulate matter less than 10 micron in size $(PM_{10}).$ The objective of this paper were to investigate the effectiveness of Artificial Neural Network (ANN) model with Back Propagation Neural Network (BPNN) for predicting the ambient air quality for air quality monitoring in states of Malaysia. The measurement activities are carried at Jalan Tasek in Perak, Nilai in Negeri Sembilan and Jerantut in Pahang. The data collected comprises of data for the previous two months, beginning from November 2004. The ambient air quality plays an important role in evaluating the air quality. The artificial neural network simplifies and speeds up the computation of the ambient air quality, as compared to the currently existing method. For this purposes, neural network model provides an interesting alternative to air quality monitoring. The comparison between data from model predictions and actual observations is coherent which shows that promising result based on the developed ANN model in predicting ambient air quality (AAQ) is effective and accurate.
Starting Page	115
Ending Page	119
File Size	266887
Page Count	5
File Format	PDF
ISBN	9781424405268
DOI	10.1109/SCORED.2006.4339321
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-27
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Atmospheric modeling Neural networks Artificial neural networks Carbon dioxide Predictive models Air pollution Computer networks Pollution measurement Nitrogen Monitoring
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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