Cover Image

A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality i...

Full description

Saved in:
Bibliographic Details
Main Authors: Hassan, Mustafa Hamid, Mostafa, Salama A., Mustapha, Aida, Saringat, Mohd. Zainuri, Al-Rimy, Bander Ali Saleh, Saeed, Faisal, Eljialy, A. E. M., Jubair, Mohammed Ahmed
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/104202/1/BanderAliSaleh2022_ANewCollaborativeMultiAgent.pdf
http://eprints.utm.my/104202/
http://dx.doi.org/10.3390/su14010510
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.104202
record_format eprints
spelling my.utm.1042022024-01-18T00:24:08Z http://eprints.utm.my/104202/ A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment Hassan, Mustafa Hamid Mostafa, Salama A. Mustapha, Aida Saringat, Mohd. Zainuri Al-Rimy, Bander Ali Saleh Saeed, Faisal Eljialy, A. E. M. Jubair, Mohammed Ahmed Q Science (General) Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index‐based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time‐series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series‐based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short‐term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi‐agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real‐world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models. MDPI 2022-01-01 Article PeerReviewed application/pdf en http://eprints.utm.my/104202/1/BanderAliSaleh2022_ANewCollaborativeMultiAgent.pdf Hassan, Mustafa Hamid and Mostafa, Salama A. and Mustapha, Aida and Saringat, Mohd. Zainuri and Al-Rimy, Bander Ali Saleh and Saeed, Faisal and Eljialy, A. E. M. and Jubair, Mohammed Ahmed (2022) A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment. Sustainability, 14 (1). pp. 1-21. ISSN 2071-1050 http://dx.doi.org/10.3390/su14010510 DOI:10.3390/su14010510
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Hassan, Mustafa Hamid
Mostafa, Salama A.
Mustapha, Aida
Saringat, Mohd. Zainuri
Al-Rimy, Bander Ali Saleh
Saeed, Faisal
Eljialy, A. E. M.
Jubair, Mohammed Ahmed
A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment
description Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index‐based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time‐series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series‐based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short‐term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi‐agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real‐world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.
format Article
author Hassan, Mustafa Hamid
Mostafa, Salama A.
Mustapha, Aida
Saringat, Mohd. Zainuri
Al-Rimy, Bander Ali Saleh
Saeed, Faisal
Eljialy, A. E. M.
Jubair, Mohammed Ahmed
author_facet Hassan, Mustafa Hamid
Mostafa, Salama A.
Mustapha, Aida
Saringat, Mohd. Zainuri
Al-Rimy, Bander Ali Saleh
Saeed, Faisal
Eljialy, A. E. M.
Jubair, Mohammed Ahmed
author_sort Hassan, Mustafa Hamid
title A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment
title_short A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment
title_full A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment
title_fullStr A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment
title_full_unstemmed A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment
title_sort new collaborative multi‐agent monte carlo simulation model for spatial correlation of air pollution global risk assessment
publisher MDPI
publishDate 2022
url http://eprints.utm.my/104202/1/BanderAliSaleh2022_ANewCollaborativeMultiAgent.pdf
http://eprints.utm.my/104202/
http://dx.doi.org/10.3390/su14010510
_version_ 1789424393393274880
score 13.211869

Similar Items