Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong
Introduction
Hong Kong is recognized as one of the most developed metropolitans in the Asian region and has probably the highest population density in the world (approximately . With continuous economic development and population increase, a series of severe problems relating to the environmental benign and protection has attracted much attention than ever before, e.g., air pollution, noise pollution, shortage of land resources, waste and sewage disposal, etc. Among these, air pollution has direct effect on human health through exposure to pollutants at high concentration level existing in ambient. The main sources of air pollution in Hong Kong are vehicle emissions, especially from diesel vehicles. Diesel emissions contain three major pollutants: respirable suspended particulate (RSP), nitrogen oxides, and hydrogen carbons. In Hong Kong, almost all commercial vehicles, such as public buses, goods vehicles, and taxies, run on diesel fuel, which emit high levels of RSP and other pollutants. Statistically, vehicles running on diesel contribute 98% of all emitted particulates. With the economic growth, the rapid developments in road networks and the increasing need for individual mobility, traffic flow will increase continuously. The air pollution due to vehicle exhausts will be the most severe social problem in Hong Kong within the next few years [8], [9].
Modelling pollutant levels in ambient includes using a variety of approaches. The most conventional method is to use computational fluid dynamics (CFD) approach to simulate the airflow pattern and pollutant concentration by solving a highly coupled, non-linear, partial differential equation set. Such method demands huge computing cost, which, sometimes, cause difficulties in computational convergence, especially for treating large space cases, and experimental validation, which is even more expensive and difficult to achieve due to the scaling inconsistency. In recent years, the use of neural networks, in particular, the multi-layer perceptrons, which can be trained to approximate virtually any smooth, measurable function, presents potential and becomes popular in practice. Unlike other statistical techniques, the multi-layer perceptrons take no prior assumptions concerning the data distribution. It can simulate highly non-linear functions and can be trained to accurately generalize/forecast, if presented with new, the unseen data. These features of the multi-layer perceptrons make it very attractive to be used in environmental science, e.g., modelling pollutant tendency in ambient. To date, the neural network method featured with multi-layer perceptrons has been applied in the field of air-quality prediction in recent years, and some promising results have been reported [2], [3], [6], [7], [13], [14], [15], [16], [17].
Similar to all neural network models, the multi-layer perceptrons must be trained with the sample solutions, i.e., to obtain prediction ability with a training set. Concerning the training, the back-propagation (BP) algorithm is the most commonly used perceptron to perform such task and has been used by some researchers in their studies [7], [5], [17]. BP algorithm is a gradient-based method, hence some inherent problems (or difficulties) are frequently encountered in the use of this algorithm, e.g., very slow convergence speed in training, easily to get stuck in a local minimum, etc. Some techniques are therefore introduced in an attempt to resolve these drawbacks, but, to date, all of them are still far from satisfaction [6]. The particle swarm optimization (PSO) perceptron, developed by Eberhart and Kennedy in recent years [12], is a method for optimizing hard numerical functions based on metaphor of human social interaction [4], [10], [11]. Originally developed as a tool for simulating social behavior, the PSO algorithm has been accepted as a computational intelligence technique closely related to evolutionary algorithms [1], [5], [13]. In this study, PSO is adopted to train the multi-layer perceptrons and to predict air-quality parameters. As a result, a PSO-based neural network approach is developed. Such approach is validated with four practical cases of predicting air-quality parameters in downtown area of Hong Kong based on the original pollutant data supplied by Hong Kong Environmental Protection Department (HKEPD).
Section snippets
Mathematical basis of multi-layer perceptrons
A multi-layer perceptron consists of a system of simple interconnected neurons, or nodes, as illustrated in Fig. 1. It is a model representing a non-linear mapping between input and output vectors. The nodes are connected by weights and output signals, which are a function of the sum of the inputs to the node modified by a simple non-linear transfer, or activation function. It is the superposition of many simple non-linear transfer functions that enables the multi-layer perceptron to
PSO algorithm and its adaptation to training the multi-layer perceptrons
Particle swarm optimization is a population search method, which resembles a school of flying birds. Particles, also called individuals in evolutionary algorithms (EAs), are candidate solutions to the problem to be solved. In a PSO system, instead of using genetic operators as in EAs, a population of these individuals is “evolved” by cooperation and competition among the individuals themselves through generations. Each particle adjusts its flying according to its own flying experience and its
Results and discussion
The original air-quality data used in this study come from the Hong Kong Environmental Protection Department (HKEPD). There are totally 14 air-pollutant gaseous monitoring stations distributed over the whole territory of Hong Kong established by HKEPD. In this study, only the data of 1999 from the Causeway Bay Roadside Gaseous Monitoring Station is available and chosen for the numerical simulation. The proposed PSO-based multi-layer perceptrons are examined in comparison with BP-based ones in
Conclusion
A PSO-based neural network approach is developed for modelling air-pollutant parameters. The approach takes a novel kind of optimization algorithm, i.e., particle swarm optimization algorithm, to train the multi-layer perceptrons. The feasibility and effectiveness of this new approach is validated and illustrated by four practical cases of modelling air pollutant levels. The data measured at a roadside Gaseous Monitory Station by the HKEPD are chosen as the original data to testify the
Acknowledgements
The work described in this paper was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 1013/02E] and a Strategic Research Grant #7001086(BC) from City University of Hong Kong, HKSAR. The provision of original data from Environmental Protection Department, HKSAR, is also appreciated.
Dr. LU, Jane Weizhen Assistant Professor, Department of Building and Construction, City University of Hong Kong, Kowloon Tong, Kowloon, HK Tel.: (852) 2784 4316, Fax.: (852) 2788 7612, E-Mail: [email protected]. Dr. Lu has been working as assistant professor at City University of Hong Kong since October 1996. She obtained BSc and MEng in Xi'an Jiaotong University in 1982 and 1985. She was appointed as Assistant Lecturer and Lecturer in the same University thereafter. From 1990 to 1993, She
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Dr. LU, Jane Weizhen Assistant Professor, Department of Building and Construction, City University of Hong Kong, Kowloon Tong, Kowloon, HK Tel.: (852) 2784 4316, Fax.: (852) 2788 7612, E-Mail: [email protected]. Dr. Lu has been working as assistant professor at City University of Hong Kong since October 1996. She obtained BSc and MEng in Xi'an Jiaotong University in 1982 and 1985. She was appointed as Assistant Lecturer and Lecturer in the same University thereafter. From 1990 to 1993, She worked as a research Scientist in National Engineering Laboratory, UK. She joined a consultancy project of multi-phase flow for UK Oil Companies. In 1993, she worked as a Research Officer in De Montfort University, UK. The research project was analysis of airflow and aerosol particle distribution in buildings, which was sponsored by UK Engineering & Physical Science Research Council (EPSRC). She finished Ph.D. project in October 1995, took Lecturer post in Dept. of Building Services Engineering, Hong Kong Polytechnic University in November 1995 and worked actively in teaching and research. The research includes an investigation of aerosol particle distribution in indoor and outdoor environment for The Consumer Council (HK). She is currently teaching Building Services Engineering, Thermal Fluids, Environmental Sciences, etc. She has many years research experiences in air quality aspect including both numerical simulation and experimental study. Her main research interests include: air quality, air infiltration, HVAC system, wind effect on high-rise buildings, application of Computational Fluid Dynamics and computation intelligence in various engineering disciplines including building engineering, chemical & environmental engineering, mechanical engineering, power engineering, wind engineering, etc.
Dr. Fan obtained BEng and Ph.D. from Xi'an Jiaotong University in 1983 and 2000 respectively. He joined Yangzi Petroleum and Chemical Corporation of SINOPEC after obtaining BEng and worked there for ten years. He was involved in major production projects and responsible for technical inspection of production operation. Dr. Fan returned Xi'an Jiaotong University to carry on a Ph.D. study after obtaining ten years practical experiences. His Ph.D. research includes CFD analysis, Artificial Intelligent Computation and its application in fluid machinery design. He then joined Building and Construction Dept, City University of Hong Kong as a researcher. His works in CityU mainly involve Soft Computational methods and its application in Atmospheric and Environmental Engineering. Dr. Fan is currently a research staff in Department of Information Technology, Lappeenranta University of Technology, Finland.
Dr. SM. Lo is an associate professor at the Department of Building and Construction, City University of Hong Kong. He obtained his Ph.D. in Architecture from the University of Hong Kong. Before starting his teaching career, he has worked for the Buildings Department, Hong Kong Government for many years and has extensive experience in building construction and drafting legislative proposals and codes of practices. He is currently a member of the Contractors’ Registration Committee. His main research interests include building design and environment, fire safety engineering, computer-aided design, etc. and holds many research grants supported by Hong Kong Research Grant Council for studying evacuation, fire risk analysis, wayfinding modelling, intelligent understanding of CAD plans, etc.
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Visiting scholar of Building and Construction Department, City University of Hong Kong.