Classification of air quality monitoring stations using fuzzy similarity measures: A case study

Kamal Jyoti Maji, Anil Kumar Dikshit, Ashok Deshpande

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Citation (Scopus)

Abstract

The objective of designing and installation air quality monitoring network (AQMN) is to reduce network density with a view to acquire maximum information on air quality with minimum expenses. In spite of the best research efforts, there has been no general acceptance of any method for deciding the number of stations. Majority of the cities have, therefore, installed monitoring stations with their own guidelines. The present paper presents a useful formulation for classification of the existing air quality monitoring stations (AQMS) using fuzzy similarity measures. The case study has been demonstrated by applying the methodology to the already-installed AQMS in Delhi, India.
Original languageEnglish
Title of host publicationRecent Developments and New Direction in Soft-Computing Foundations and Applications
Subtitle of host publicationSelected Papers from the 4th World Conference on Soft Computing, May 25-27, 2014, Berkeley
EditorsLotfi A. Zadeh, Ali M. Abbasov, Ronald R. Yager, Shahnaz N. Shahbazova, Marek Z. Reformat
Place of PublicationSwitzerland
PublisherSpringer
Pages489-501
Number of pages13
Volume342
ISBN (Electronic)9783319322292
ISBN (Print)9783319322278
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameStudies in Fuzziness and Soft Computing
PublisherSpringer
ISSN (Print)1434-9922

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