Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data

Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Volumn , Issue , 2014, Pages 760-765

  Ong, Bun Theang ^a   Sugiura, Komei ^a   Zettsu, Koji ^a  

^a NATIONAL INSTITUTE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY   (Japan)

Author keywords

Deep Learning; Environmental Sensor Data; Fine Particulate Matter; Pre training; Recurrent Neural Networks; Time Series Prediction

Indexed keywords

DEEP NEURAL NETWORKS; FORECASTING; MONITORING; NUMERICAL METHODS; PARTICLES (PARTICULATE MATTER); TIME SERIES;

DEEP LEARNING; ENVIRONMENTAL MONITORING; ENVIRONMENTAL SENSOR; ENVIRONMENTAL SENSOR DATA; FINE PARTICULATE MATTER; PM 2.5; PRE-TRAINING; SENSORS DATA; TIME SERIES PREDICTION; TRAINING METHODS;

RECURRENT NEURAL NETWORKS;

EID: 84921802982     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BigData.2014.7004302     Document Type: Conference Paper

References (25)

1. Y. Zheng, F. Liu, and H.-P. Hsieh, "U-Air: When urban air quality inference meets big Data," in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD '13. New York, NY USA: ACM, 2013, pp. 1436-1444.
2. M. Dergham, S. Billet, A. Verdin, D. Courcot, F. Cazier P. Shirali, and G. Garcon, Advanced Materials Research 2011, Vol. 324, ch. Chapter III: Applications, pp. 489-492.
3. S. McKeen, S. H. Chung, J. Wilczak, G. Grell, I. Djalalova S. Peckham, W. Gong, V. Bouchet, R. Moffet, Y. Tang G. R. Carmichael, R. Mathur, and S. Yu, "Evaluation of several pm2.5 forecast models using Data collected During the icartt/neaqs 2004 field study," Journal of Geophysical Research: Atmospheres, Vol. 112, pp. 1-20, 2007.
4. M. Budde, R. El Masri, T. Riedel, and M. Beigl, "Enabling low-cost particulate matter measurement for participatory sensing scenarios," in Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia, ser. MUM '13, Vol. 19. New York, NY, USA: ACM, 2013, pp. 1-10.
5. S. Bergen, L. Sheppard, P. D. Sampson, S.-Y. Kim M. Richards, S. Vedal, J. D. Kaufman, and A. A. Szpiro, "A national prediction model for pm2.5 component exposures and measurement error-corrected health effect inference," Environmental Health Perspectives, Vol. 121, no. 9, pp. 1071-1025.
6. "World Health Organization. Health effects of particulate matter," http://www.euro.who.int/ Data/assets/pdf file/0006/ 189051/Health-effects-of-particulate-matter-final-Eng.pdf 2013.
7. "Air Quality Expert Group. Fine particulate matter (PM2.5) in the UK," http://uk-Air.defra.gov.uk, Tech. Rep., 2012.
8. T. M. Shinji Wakamatsu and A. Ito, "Air pollution trends in japan between 1970 and 2012 and impact of urban air pollution countermeasures," Asian Journal of Atmospheric Environment, Vol. 7, no. 4, pp. 177-190, December 2013.
9. M. Adya and F. Collopy, "How effective are neural networks at forecasting and prediction? A review and evaluation," Journal of Forecasting, Vol. 17, pp. 481-495, 1998.
10. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol. 9, no. 8, pp. 1735-1780, 1997.
11. H. Jaeger, "The ''echo state'' approach to analysing and training recurrent neural networks-with an erratum note," German National Research Center for Information Technology, Tech. Rep., 2001.
12. A. Graves and J. Schmidhuber, "Offline handwriting recognition with multidimensional recurrent neural networks," in Advances in Neural Information Processing Systems, 2008 pp. 545-552.
13. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle "Greedy layer-wise training of Deep networks," in Advances in Neural Information Processing Systems 19, B. Schölkopf J. Platt, and T. Hoffman, Eds. Cambridge, MA: MIT Press 2007, pp. 153-160.
14. G. E. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for Deep belief nets," Neural Computation, Vol. 18 no. 7, pp. 1527-1554, July 2006.
15. G. E. Hinton and R. R. Salakhutdinov, "Reducing the Dimensionality of Data with neural networks," Science, Vol. 313, no. 5786, pp. 504-507, July 2006.
16. L. Deng, G. Hinton, and B. Kingsbury, "New types of Deep neural network learning for speech recognition and related applications: an overview," in Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2013, pp. 8599-8603.
17. P. Tino, M. Cernansky, and L. Benuskova, "Markovian architectural bias of recurrent neural networks," IEEE Transactions on Neural Networks, Vol. 15, no. 1, pp. 6-15, Jan 2004.
18. Y. Bengio, N. Boulanger-Lewandowski, and R. Pascanu "Advances in optimizing recurrent networks," in Proceedings of the 2013 IEEE International Conference on Acoustics Speech and Signal Processing, May 2013, pp. 8624-8628.
19. S. Young and I. Arel, "Recurrent clustering for unsupervised feature extraction with application to sequence Detection," in Proceedings of the 2012 11th International Conference on Machine Learning and Applications (ICMLA), Vol. 2, Dec 2012, pp. 54-55.
20. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol "Extracting and composing robust features with Denoising autoencoders," in Proceedings of the 25th International Conference on Machine Learning, ser. ICML '08. New York NY, USA: ACM, 2008, pp. 1096-1103.
21. "National Institute for Environmental Studies. Visual Atmospheric Environment Utility System," http://envgis5. nies.go.jp/osenyosoku/.
22. H. Bourlard and Y. Kamp, "Auto-Association by multilayer perceptrons and singular Value Decomposition," Biological Cybernetics, Vol. 59, no. 4-5, pp. 291-294, 1988.
23. H. Cheng, P.-N. Tan, J. Gao, and J. Scripps, "Multistepahead time series prediction," in Advances in Knowledge Discovery and Data Mining, ser. Lecture Notes in Computer Science, W.-K. Ng, M. Kitsuregawa, J. Li, and K. Chang, Eds. Springer Berlin Heidelberg, 2006, Vol. 3918, pp. 765-774.
24. "National Institute for Environmental Studies," http://www. nies.go.jp/gaiyo/index-e.html/.
25. "Japan Meteorological Agency," http://www.jma.go.jp/jma/ indexe.html/