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Sustainability 2017, 9(1), 134; doi:10.3390/su9010134

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

1
Department of Geospatial Information Systems, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran
2
Center of Excellence in Geospatial Information Technology, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967 15433, Iran
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 27 November 2016 / Revised: 3 January 2017 / Accepted: 13 January 2017 / Published: 18 January 2017
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Abstract

Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners. View Full-Text
Keywords: environmental health impact assessment; hierarchical fuzzy inference system; air pollution modeling; transportation planning environmental health impact assessment; hierarchical fuzzy inference system; air pollution modeling; transportation planning
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Tashayo, B.; Alimohammadi, A.; Sharif, M. A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning. Sustainability 2017, 9, 134.

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