Application of Rough Sets Theory in Air Quality Assessment

This paper analyses rough sets approaches to air quality assessment in given locality of the Czech Republic (CR). Original data for modeling we obtained from the daily observation of air polluting substances concentrations in town Pardubice. Two applications were used for decision rules computation and achieved results are compared and analysed. Output of this paper is the proposal how to assign an air quality index (AQI) to the selected locality, on the basis of various attributes.

[1]  Andrzej Skowron,et al.  Rough-Fuzzy Hybridization: A New Trend in Decision Making , 1999 .

[2]  J. Panus,et al.  Genetic algorithms for optimization of thematic regional clusters , 2007, EUROCON 2007 - The International Conference on "Computer as a Tool".

[3]  W. J. Ter Keurs,et al.  Regional Opportunities for Sustainable Development , 1999 .

[4]  Raymond R. Tan,et al.  Application of rough sets for environmental decision support in industry , 2008 .

[5]  Hiroshi Sakai,et al.  On Possible Rules and Apriori Algorithm in Non-deterministic Information Systems , 2006, RSCTC.

[6]  C. J. M. Musters,et al.  Regional Opportunities for Sustainable Development: Theory, Methods, and Applications , 1999 .

[7]  Ryszard Tadeusiewicz,et al.  Artificial Intelligence and Soft Computing - ICAISC 2006, 8th International Conference, Zakopane, Poland, June 25-29, 2006, Proceedings , 2006, International Conference on Artificial Intelligence and Soft Computing.

[8]  Zhi-Hua Zhou,et al.  Extracting symbolic rules from trained neural network ensembles , 2003, AI Commun..

[9]  Zdzisław Pawlak.  A Primer on Rough Sets: A New Approach to Drawing Conclusions from Data , 2002 .

[10]  Marion Hersh,et al.  Mathematical modelling for sustainable development , 2005 .

[11]  R. Andoga,et al.  Intelligent Approaches in Modeling and Control of a Small Turbojet Engines , 2007, 2007 11th International Conference on Intelligent Engineering Systems.

[12]  J. Vascak,et al.  Path planning in dynamic environment using Fuzzy Cognitive Maps , 2008, 2008 6th International Symposium on Applied Machine Intelligence and Informatics.

[13]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[14]  Sinh Hoa Nguyen,et al.  Hierarchical Rough Classifiers , 2007, RSEISP.

[15]  Jirava Pavel,et al.  Classification model based on rough and fuzzy sets theory , 2007 .

[17]  Pericles A. Mitkas,et al.  Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support , 2003 .

[18]  Tetsuya Murai,et al.  A Method of Generating Decision Rules in Object-Oriented Rough Set Models , 2006, RSCTC.

[19]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

[20]  Yoichi Hayashi,et al.  Greedy rule generation from discrete data and its use in neural network rule extraction , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[21]  Ian Witten,et al.  Data Mining , 2000 .

[22]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[23]  Jacek M. Zurada,et al.  Artificial Intelligence and Soft Computing, 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part I , 2010, International Conference on Artificial Intelligence and Soft Computing.

[24]  Marcin S. Szczuka,et al.  A New Version of Rough Set Exploration System , 2002, Rough Sets and Current Trends in Computing.