HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Bougoudis, Ilias; Demertzis, Konstantinos; Iliadis, Lazaros
• Is Part Of: Neural computing & applications. Volume 27:Issue 5(2016)
• Page Start: 1191
• Page End: 1206
• Publication Date: 2016-07
• Subjects: Ensembles learning -- Ensembles of classifiers -- Fuzzy inference systems -- Feedforward neural network -- Random forest -- Air pollution; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1927-7
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.