nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers

verfasst von : Ilias Bougoudis, Konstantinos Demertzis, Lazaros Iliadis, Vardis-Dimitris Anezakis, Antonios Papaleonidas

Erschienen in: Engineering Applications of Neural Networks

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Air pollution is directly linked with the development of technology and science, the progress of which besides significant benefits to mankind it also has adverse effects on the environment and hence on human health. The problem has begun to take worrying proportions especially in large urban centers, where 60,000 deaths are reported each year in Europe’s towns and 3,000,000 worldwide, due to long-term air pollution exposure (exposure of the European Agency for the Environment http://www.eea.europa.eu/). In this paper we propose a novel and flexible hybrid machine learning system that combines Semi-Supervised Classification and Semi-Supervised Clustering, in order to realize prediction of air pollutants outliers and to study the conditions that favor their high concentration.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Choice of Best Samples for Building Ensembles in Dynamic Environments

Nächstes Kapitel Predicting Abnormal Bank Stock Returns Using Textual Analysis of Annual Reports – a Neural Network Approach

Bougoudis, I., Iliadis, L., Papaleonidas, A.: Fuzzy inference ANN ensembles for air pollutants modeling in a major urban area: the case of Athens. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 1–14. Springer, Heidelberg (2014)

Bougoudis, I., Iliadis, L., Spartalis, S.: Comparison of self organizing maps clustering with supervised classification for air pollution data sets. In: Iliadis, L., Maglogiannis, L., Papadopoulos, H. (eds.) AIAI 2014. IFIP AICT, vol. 436, pp. 424–435. Springer, Heidelberg (2014)

Bougoudis, I., Demertzis, K., Iliadis, L.: Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning. Integr. Comput.-Aided Eng. 23(2), 115–127 (2016). doi:10.3233/ICA-150505. IOS PressCrossRef

Bougoudis, I., Demertzis, K., Iliadis, L.: HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. EANN Neural Comput. Appl. 1–16 (2016). doi:10.1007/s00521-015-1927-7

Roy, S.: Prediction of particulate matter concentrations using artificial neural network. Resour. Environ. 2(2), 30–36 (2012). doi:10.5923/j.re.20120202.05 CrossRef

Robles, L.A., Ortega, J.C., Fu, J.S., Reed, G.D., Chow, J.C., Watson, J.G., Moncada-Herrera, J.A.: A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmos. Environ. 42(35), 8331–8340 (2008)CrossRef

Ordieres Meré, J.B., Vergara González, E.P., Capuz, R.S., Salaza, R.E.: Neural network prediction model for fine particulate matter (PM). Environ. Model Softw. 20, 547–559 (2005)CrossRef

Wahab, A., Al-Alawi, S.M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ. Model. 17, 219–228 (2002)CrossRef

Paschalidou, A., Iliadis, L., Kassomenos, P., Bezirtzoglou, C.: Neural modeling of the tropospheric ozone concentrations in an urban site. In: Proceedings of 10th International Conference Engineering Applications of Neural Networks, pp. 436–445 (2007)

10.

Ozcan, H.K., Bilgili, E., Sahin, U., Bayat, C.: Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks. Advances in Atmospheric Sciences, vol. 24, pp. 907–914. Springer, Heidelberg (2007)

11.

Ozdemir, H., Demir, G., Altay, G., Albayrak, S., Bayat, C.: Prediction of tropospheric ozone concentration by employing artificial neural networks. Environ. Eng. Sci. 25(9), 1249–1254 (2008)CrossRef

12.

Inal, F.: Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey. CLEAN – Soil Air Water 38(10), 897–908 (2010)MathSciNetCrossRef

13.

Paoli, C.: A neural network model forecasting for prediction of hourly ozone concentration in Corsica. In: Proceedings IEEE of 10th International Conference on EEEIC (2011)

14.

Kadri, C., Tian, F., Zhang, L., Dang, L., Li, G.: Neural network ensembles for online gas concentration estimation using an electronic nose. IJCS 10(2), 1 (2013)

15.

Vong, C.-M., Ip, W.-F., Wong, P.-K., Yang, J.-Y.: Short-term prediction of air pollution in Macau using support vector machines. J. Control Sci. Eng. 2012, 4 (2012). Article ID 518032CrossRefMATH

16.

Xiao, F., Li, Q., Zhu, Y., Hou, J., Jin, L., Wang, J.: Artificial neural networks forecasting of PM 2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 107, 118–128 (2015). doi:10.1016/j.atmosenv.2015.02.030. ElsevierCrossRef

17.

Zabkar, R., Cemas, D.: Ground-level ozone forecast based on ML. AIR040051 (2004)

18.

Lopez-Rubio, E., Palomo, E.J., Dominguez, E.: Bregman divergences for growing hierarchical self-organizing networks. Int. J. Neural Syst. 24, 4 (2014). 1450016CrossRef

19.

Menendez, H., Barrero, D.F., Camacho, D.: A genetic graph-based approach to the partitional clustering. Int. J. Neural Syst. 24, 3 (2014). 1430008CrossRef

20.

Donos, C., Duemoelmann, M., Schulze-Bonhage, A.: Early seizure detection algorithm based on intractable EEG and random forest classification. IJNS 25, 5 (2015). 1550023

21.

Quirós, P., Alonso, P., Díaz, I., Montes, S.: On the use of fuzzy partitions to protect data. Integr. Comput.-Aided Eng. 21(4), 355–366 (2014)

22.

Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. Assoc. Adv. AI 29(3), 93 (2015)

23.

Driessens, K., Reutemann, P., Pfahringer, B., Leschi, C.: Using weighted nearest neighbor to benefit from unlabeled data. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 60–69. Springer, Heidelberg (2006)CrossRef

Titel: Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers
verfasst von: Ilias Bougoudis
Konstantinos Demertzis
Lazaros Iliadis
Vardis-Dimitris Anezakis
Antonios Papaleonidas
Verlag: Springer International Publishing
Buch: Engineering Applications of Neural Networks
Print ISBN: 978-3-319-44187-0

Electronic ISBN: 978-3-319-44188-7

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-44188-7_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"