Forecasting urban air pollution using HMM-fuzzy model

Authors:
M. Maruf Hossain;Md. Rafiul Hassan;Michael Kirley
Affiliations:
Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia;Department of Computer Science and Software Engineering, The University of Melbourne, Victoria, Australia
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

Citing 4
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Abstract

In this paper, we introduce a Computational Intelligence (CI)-based method to model an hourly air pollution forecasting system that can forecast concentrations of airborne pollutant variables. We have used a hybrid approach of Hidden Markov Model (HMM) with fuzzy logic (HMM-fuzzy) to model hourly air pollution at a location related to its traffic volume and meteorological variable. The forecasting performance of this hybrid model is compared with other common tool based on Artificial Neural Network (ANN) and other fuzzy tool where rules are extracted using subtractive clustering. This research demonstrates that the HMM-fuzzy approach is effectively able to model an hourly air pollution forecasting system.