A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment

Mustafa Hamid Hassan, Salama A. Mostafa, Aida Mustapha, Mohd Zainuri Saringat, Bander Ali Saleh Al-rimy, Faisal Saeed, A.E.M. Eljialy and Mohammed Ahmed Jubair
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Mustafa Hamid Hassan: Faculty of Computer Science and Information Technology, Universiti Tun Hussin Onn Malaysia, Parit Raja 84600, Malaysia
Salama A. Mostafa: Faculty of Computer Science and Information Technology, Universiti Tun Hussin Onn Malaysia, Parit Raja 84600, Malaysia
Aida Mustapha: Faculty of Applied Sciences and Technology, Universiti Tun Hussein Onn Malaysia, Panchor 84500, Malaysia
Mohd Zainuri Saringat: Faculty of Computer Science and Information Technology, Universiti Tun Hussin Onn Malaysia, Parit Raja 84600, Malaysia
Bander Ali Saleh Al-rimy: School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Faisal Saeed: School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK
A.E.M. Eljialy: Department of Information System, College of Computer Engineering & Sciences, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia
Mohammed Ahmed Jubair: Faculty of Computer Science and Information Technology, Universiti Tun Hussin Onn Malaysia, Parit Raja 84600, Malaysia

Sustainability, 2022, vol. 14, issue 1, 1-21

Abstract: 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.

Keywords: air quality index; air pollution; risk assessment; autoregressive integrated moving average; Monte Carlo simulation; multi-agent system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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