You are here

Home » Content

Machine Learning Algorithms in Air Quality Index Prediction

Journal Name:

  • International Journal of Science and Engineering Investigations

Publication Year:

  • 2017

Key Words:

Author Name
Abstract (2. Language): 
Urban air pollution is one of the biggest global problems at this moment. It has effect on human’s most significant element of life- oxygen. Air pollution increases risk of diseases and mortality through respiratory and cardiovascular impact, so understanding and predicting of Air Quality Index is significant public health challenge. In this paper we made comparison between three simple machine learning algorithms, neural network, k-nearest neighbor and decision tree. The results are promising and it was proven that these algorithms are very efficient in predicting Air Quality Index.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-machine-learning-algorithms-air-quality-index-prediction.pdf
  • 12
123
127
  • English

REFERENCES

References: 

[1] Christopher M. Bishop, “Neural Networks for Pattern Recognition”, Oxford, 1995
[2] Nagendra, S.S.; Khare, M. “Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions”. Ecol. Model. 2006, 190, 99–115 [3] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals, “Understanding deep learning requires rethinking generalization”, ICLR 2017
[4] A., Nielsen, Michael (2015). “Neural Network and Deep learning”, NeuralNetworksAndDeepLearning.com
[5] Bramer, M. “Principles of data mining”, Springer-Verlag, London, 2013.
[6] Altman N.S. (1992). “An introduction to kernel and nearest-neihbor nonparametric regression”
[7] Samworth R.J. (2012). “Optimal weighted nearest neighbor classifiers”.
[8] D. Coomans; D.L. Massart (1982). “Alternative k-Nearest neighbor rules in supervised pattern recognition: Part 1. k-Nearest neighbor classification by using alternative voting rules”. Analytica Chimica Acta
[9] Veljanovska, K. “Machine Learning Algorithms Comparison”, International Journal of Engineering and Technical Research (IJETR), Vol.7, No.11, 2017
[10] Veljanovska, K. “Analysis of Machine Learning Algorithms Performance for Real World Concept”, International Journal of Advanced Research in Science and Engineering (IJARSE), Vol.06, No.12, 2017
[11] Mitchell, T. “Machine Learning”, McGraw-Hill, New York, USA, 1997
[12] Sayali D. Jadhav, H.P. Channe, “Comparative Study of k-NN, Naïve Bayes and Decision Tree Classification Technologies”, International Journal of Science and Research, 2013
[13] Kattariya Kujaroentavon, Supapom Kiattisin, Adisom Leelasantitham, Sotarat Thammaboosadee, “Air quality classification in Thailand based on decision tree”, Biomedical Engineering International Conference (BMEiCON), 2014
[14] Breiman, Leo; Friedman, J.H; Olshen, R.A; Stone, C.J. (1984). “Classification and regression trees”. Montrey, CA: Wadsworth & Brooks/Cole Advanced Books & Software

Thank you for copying data from http://www.arastirmax.com