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Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data

  • Tzu-Yi PaiEmail author
  • Moo-Been Chang
  • Shyh-Wei Chen
Chapter
Part of the Studies in Big Data book series (SBD, volume 8)

Abstract

For controlling air pollution, the Taiwan Environmental Protection Administration (TEPA) installed automatic air quality monitoring stations (AQMSs) and TEPA prescribed the industries to install continuous emission monitoring systems (CEMS). By 2014, there were a total of 76 AQMS and 351 CEMS in the entire nation. Therefore, the huge amount of air quality monitoring data forms big data. The processing, interpretation, collection and organization of air quality monitoring big data (AQMBD) have emerged in air quality control including industry management, traffic reduction, and residential health. In this chapter, the application of computational intelligence on analysis of air quality monitoring big data was reviewed worldwide. Additionally, the application of computational intelligence (CI) including artificial neural network, fuzzy theory, and adaptive network-based fuzzy inference system (ANFIS) was discussed. Finally, the implementation of CI on AQMBD granular computing was proposed.

Keywords

Computational intelligence Air quality monitoring big data Artificial neural network Swarm intelligence 

Notes

Acknowledgments

The authors are grateful to the National Science Council of Taiwan, R.O.C. for financial support under the Grant Number NSC101-2621-M-142-001-MY2.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Master Program of Environmental Education and Management, Department of Science Application and DisseminationNational Taichung University of EducationTaichungRepublic of China
  2. 2.Institute of Environmental EngineeringNational Central UniversityChungliRepublic of China
  3. 3.Environmental Protection BureauTaoyuan County GovernmentTaoyuanRepublic of China

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