Abstract
Air pollution is a major problem in modern cities and developing countries. Fine particulate matter (PM2.5) is a growing public health concern and become the most serious air pollution. In this study, we formulate the PM2.5 inference problem in conventional environmental sensors as a sequence-to-sequence problem. We adopt the encoder-decoder LSTM (Long short term memory) framework to solve the PM2.5 inference problem. A novel width-variable window attention mechanism is proposed for the encoder-decoder LSTM system. The proposed method learn the position and width of the attention window simultaneously. The proposed method is evaluated on large scale data and the experimental results show that it achieves better performance on two datasets with different concentration of PM2.5.
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References
Dockery, D.W., Xu, X., Spengler, J.D.: An association between air pollution and mortality in six U.S. cities. N. Engl. J. Med. 329, 1753–1759 (1993)
Pope, C.A., Bates, D.V., Raizenne, M.E.: Health effects of particulate air pollution: time for reassessment. Environ. Health Perspect. 103, 472–480 (1995)
Mage, D., Ozolins, G., Peterson, P.: Urban air pollution in megacities of the world. Atmos. Environ. 30, 681–686 (1996)
Hamra, G.B., Guha, N., Cohen, A.: Outdoor particulate matter exposure and lung cancer: a systematic review and meta-analysis. Environ. Health Perspect. 122, 906–912 (2014)
Takasu, R.: Development of compact and low-cost PM2.5 mass concentration measurement equipment and its application to high-frequency mobile monitoring in a local area. In: 2016 International Conference on Agriculture, Energy and Environment Engineering, Bangkok (2016)
Oprea, M., Mihalache, S.F., Popescu, M.: A comparative study of computational intelligence techniques applied to PM2. 5 air pollution forecasting. In 6th International Conference on Computers Communications and Control, Oradea, Romania, pp. 103–108 (2016)
Park, D., Kwon, S.B., Cho, Y.: Development and calibration of a particulate matter measurement device with wireless sensor network function. Int. J. Environ. Monit. Anal. 1, 15–20 (2013)
Gao, M., Cao, J., Seto, E.: A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2. 5 in Xi’an, China. Environ. Pollut. 199, 56–65 (2015)
Dong, Y., Wang, H., Zhang, L.: An improved model for PM2. 5 inference based on support vector machine. In: 17th International Conference on Software Engineering, Artificial Intelligence, Shanghai, China, pp. 27–31 (2016)
Srimuruganandam, B., Nagendra, S.M.S.: ANN-based PM prediction model for assessing the temporal variability of PM10, PM2. 5 and PM1 concentrations at an urban roadway. Int. J. Environ. Eng. 7, 60–89 (2015)
Zheng, Y., Liu, F., Hsieh, H.P.: U-Air: When urban air quality inference meets big data. In: 19th ACM SIGKDD International Conference on Knowledge discovery and Data Mining, New York, pp. 1436–1444 (2013)
Coffey, C.C., Pearce, T.A.: Direct-reading methods for workplace air monitoring. J. Chem. Health Saf. 17, 10–21 (2010)
Bahdanau, D., Cho, K.H., Bengio, Y.: Neural Machine Translation by Jointly Learning to Align and Translate. arXiv preprint arXiv:1409.0473 (2014)
Chorowski, J.K., Bahdanau, D., Serdyuk, D.: Attention-based models for speech recognition. In: 29th Annual Conference on Neural Information Processing Systems, Quebec, Canada, pp. 577–585 (2015)
Gregor, K., Danihelka, I., Graves, A.: DRAW: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623 (2015)
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Hou, C., Xia, Y., Sun, J., Shang, J., Takasu, R., Kondo, M. (2017). A Width-Variable Window Attention Model for Environmental Sensors. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_53
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DOI: https://doi.org/10.1007/978-3-319-70096-0_53
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