HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens

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.

[1]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[2]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Saleh M. Al-Alawi,et al.  Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks , 2002, Environ. Model. Softw..

[4]  Surendra Roy,et al.  Prediction of Particulate Matter Concentrations Using Artificial Neural Network , 2012 .

[5]  Konstantinos Demertzis,et al.  Commentary: Aedes albopictus and Aedes japonicas—two invasive mosquito species with different temperature niches in Europe , 2017, Front. Environ. Sci..

[6]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[7]  Fengchun Tian,et al.  Neural Network Ensembles for Online Gas Concentration Estimation Using an Electronic Nose , 2013 .

[8]  J. Chow,et al.  A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile , 2008 .

[9]  Lazaros S. Iliadis,et al.  Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens , 2014, EANN.

[10]  Konstantinos Demertzis,et al.  Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus , 2017, Neural Computing and Applications.

[11]  Goksel Demir,et al.  Prediction of Tropospheric Ozone Concentration by Employing Artificial Neural Networks , 2008 .

[12]  Feng Wan,et al.  Applying Ensemble Learning Techniques to ANFIS for Air Pollution Index Prediction in Macau , 2012, ISNN.

[13]  Konstantinos Demertzis,et al.  Machine learning use in predicting interior spruce wood density utilizing progeny test information , 2017, Neural Computing and Applications.

[14]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[15]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[16]  Jean-Michel Poggi,et al.  Three Non-Linear Statistical Methods for Analyzing PM10 Pollution in Rouen Area , 2014 .

[17]  BougoudisIlias,et al.  HISYCOL a hybrid computational intelligence system for combined machine learning , 2016 .

[18]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[19]  Joaquín B. Ordieres Meré,et al.  Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua) , 2005, Environ. Model. Softw..

[20]  Erdem Bilgili,et al.  Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks , 2007 .

[21]  Konstantinos Demertzis,et al.  Evolving Computational Intelligence System for Malware Detection , 2014, CAiSE Workshops.

[22]  Konstantinos Demertzis,et al.  Comparative analysis of exhaust emissions caused by chainsaws with soft computing and statistical approaches , 2018, International Journal of Environmental Science and Technology.

[23]  F. Inal,et al.  Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey , 2010 .

[24]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[25]  Shikha Gupta,et al.  Identifying pollution sources and predicting urban air quality using ensemble learning methods , 2013 .

[26]  Konstantinos Demertzis,et al.  Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning , 2016, Integr. Comput. Aided Eng..

[27]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[28]  Oscar Castillo,et al.  A Method for Creating Ensemble Neural Networks Using a Sampling Data Approach , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[29]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[30]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[31]  Konstantinos Demertzis,et al.  FuSSFFra, a fuzzy semi-supervised forecasting framework: the case of the air pollution in Athens , 2018, Neural Computing and Applications.

[32]  Wei Tang,et al.  Corrigendum to "Ensembling neural networks: Many could be better than all" [Artificial Intelligence 137 (1-2) (2002) 239-263] , 2010, Artif. Intell..

[33]  L. S. Iliadis,et al.  Neural Modelling of the Tropospheric Ozone Concentrations in an Urban Site , 2007 .

[34]  Konstantinos Demertzis,et al.  Hybrid Soft Computing Analytics of Cardiorespiratory Morbidity and Mortality Risk Due to Air Pollution , 2017, ISCRAM-med.

[35]  Konstantinos Demertzis,et al.  Artificial Intelligence Applications and Innovations: 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part II , 2022, IFIP Advances in Information and Communication Technology.

[36]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[37]  G. Notton,et al.  A Neural Network model forecasting for prediction of hourly ozone concentration in Corsica , 2011, 2011 10th International Conference on Environment and Electrical Engineering.