Article: Rough set-based regionalisation in air quality monitoring Journal: International Journal of Environment and Pollution (IJEP) 2013 Vol.53 No.1/2 pp.131 - 147 Abstract: Regionalisation is to organise a large set of spatial objects into spatially contiguous regions despite optimising the homogeneity of the derived regions, while representing social and economic geography. To confront this problem, it is necessary to classify the regions to form groups that are homogeneous in air quality attributes. It is to develop a system that applies data mining techniques to study the distribution of air pollutants in Chennai, a metro city in India using vehicular ad hoc networking and map the distribution on the geographical map for effective policy making. In conventional regionalisation methods, the data points are assigned to a single region in a multidimensional attribute space affecting air pollution response. However, some data points, having distributed membership to more than one region, could not be justified and allocated to a single region. Rough set-based clustering technique is applied to regionalisation problem to resolve vague or overlapping regions. The overlapping regions are restructured to guarantee the homogeneity of the regions formed or altered. The investigations of the cluster validity tests confirm the effectiveness of rough set-based regionalisation in air quality modelling. Inderscience Publishers - linking academia, business and industry through research

Title: Rough set-based regionalisation in air quality monitoring

Authors: Komathy Karuppanan; Christina Jayakumaran

Addresses: Easwari Engineering College, Anna University, Bharathi Salai, Ramapuram, Chennai-600089, India ' Loyola-ICAM College of Engineering and Technology, Anna University, Loyola College Campus, Nungambakkam, Chennai-600034, India

Abstract: Regionalisation is to organise a large set of spatial objects into spatially contiguous regions despite optimising the homogeneity of the derived regions, while representing social and economic geography. To confront this problem, it is necessary to classify the regions to form groups that are homogeneous in air quality attributes. It is to develop a system that applies data mining techniques to study the distribution of air pollutants in Chennai, a metro city in India using vehicular ad hoc networking and map the distribution on the geographical map for effective policy making. In conventional regionalisation methods, the data points are assigned to a single region in a multidimensional attribute space affecting air pollution response. However, some data points, having distributed membership to more than one region, could not be justified and allocated to a single region. Rough set-based clustering technique is applied to regionalisation problem to resolve vague or overlapping regions. The overlapping regions are restructured to guarantee the homogeneity of the regions formed or altered. The investigations of the cluster validity tests confirm the effectiveness of rough set-based regionalisation in air quality modelling.

Keywords: regionalisation; homogeneity; air pollution; agglomerative clustering; rough sets; fuzzy clustering; cohesion; variance; air quality modelling; India; vehicular ad hoc networks; VANETs; air quality monitoring; data mining.

DOI: 10.1504/IJEP.2013.058818

International Journal of Environment and Pollution, 2013 Vol.53 No.1/2, pp.131 - 147

Received: 02 Apr 2013
Accepted: 13 Oct 2013
Published online: 28 Jun 2014 *

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Title: Rough set-based regionalisation in air quality monitoring

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