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Air quality monitoring and analysis with dynamic training using deep learning

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Abstract

Time series prediction is a challenging predictive modeling case. It is essential to have a prediction model that can adapt to dynamic data. Air quality data show a significant changing degree of spatial and temporal data. Therefore, the updated deep learning model is suitable for this case. In this paper, monitoring and analysis of air quality with dynamic training using recurrent neural network (RNN) are proposed to provide the model remains up-to-date as new data comes. In the experiments, by adjusting the model, the accuracy is enhanced. The scheduling retrained model is provided based on the expected mean absolute percentage error (MAPE) value. First, the machine learning architecture environment is being prepared. Secondly, the RNN parameters were optimized for excellent level predictive precision. Third, set and test the scheduling and MAPE value based on the MAPE’s expected value for the automatic retraining model. Finally, on the interactive map, the output is presented using R and Shiny to visualize the RNN training results.

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References

  1. Aravinth MS, Shanmugapriyaa M, Sowmya M, Arun M (2015) An efficient HADOOP frameworks SQOOP and Ambari for big data processing. Int J Innov Res Sci Technol 1(10):252–255

    Google Scholar 

  2. Bai Y, Sun Z, Deng J, Li L, Long J, Li C (2017) Manufacturing quality prediction using intelligent learning approaches: a comparative study. Sustainability 10(1):85

    Article  Google Scholar 

  3. Bivand RS, Pebesma EJ, Gómez-Rubio V, Pebesma EJ (2008) Applied spatial data analysis with R, vol 747248717. Springer, Berlin

    MATH  Google Scholar 

  4. Borthakur D et al. (2008) HDFS architecture guide. HADOOP Apache Project. http://hadoop.apache.org/docs/stable1/hdfs_design.html. Accesed on 15 May 2019

  5. Brocklebank JC, Dickey DA, Choi B (2018) SAS for forecasting time series. SAS Institute, North Carolina

    MATH  Google Scholar 

  6. Chang F, Dean J, Ghemawat S, Hsieh WC, Wallach DA, Burrows M, Chandra T, Fikes A, Gruber RE (2008) Bigtable: a distributed storage system for structured data. ACM Trans Comput Syst (TOCS) 26(2):4

    Article  Google Scholar 

  7. Connor JT, Martin RD, Atlas LE (1994) Recurrent neural networks and robust time series prediction. IEEE Trans Neural Netw 5(2):240–254

    Article  Google Scholar 

  8. Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51(1):107–113

    Article  Google Scholar 

  9. de Myttenaere A, Golden B, Grand BL, Rossi F (2016) Mean absolute percentage error for regression models. Neurocomputing 192:38–48. https://doi.org/10.1016/j.neucom.2015.12.114 Advances in artificial neural networks, machine learning and computational intelligence

    Article  Google Scholar 

  10. do Nascimento Camelo H, Lucio PS, Junior JBVL, de Carvalho PCM, dos Santos DVG (2018) Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks. Energy 151:347–357. https://doi.org/10.1016/j.energy.2018.03.077

    Article  Google Scholar 

  11. Ghemawat S, Gobioff H, Leung ST (2003) The Google file system. ACM, New York

    Google Scholar 

  12. Kleine Deters J, Zalakeviciute R, Gonzalez M, Rybarczyk Y (2017) Modeling PM2.5 urban pollution using machine learning and selected meteorological parameters. J Electr Comput Eng 2017:14. https://doi.org/10.1155/2017/5106045

    Article  Google Scholar 

  13. Kristiani E, Yang CT, Huang CY, Ko PC, Fathoni H (2020) On construction of sensors, edge, and cloud (ISEC) framework for smart system integration and applications. In: IEEE Internet of Things Journal

  14. Ku JH (2018) A study on prediction model of equipment failure through analysis of big data based on RHADOOP. Wirel Pers Commun 98(4):3163–3176

    Article  Google Scholar 

  15. Lee CF, Yang CT, Kristiani E, Tsan YT, Chan WC, Huang CY (2018) Recurrent neural networks for analysis and automated air pollution forecasting. In: International Conference on Frontier Computing, pp 50–59. Springer

  16. Lee CH, Zheng YL (2015) Automatic SQL-to-NoSQL schema transformation over the MySQL and HBase databases. In: Consumer Electronics-Taiwan (ICCE-TW), 2015 IEEE International Conference on, pp 426–427. IEEE

  17. Li X, Peng L, Hu Y, Shao J, Chi T (2016) Deep learning architecture for air quality predictions. Environ Sci Pollut Res 23(22):22408–22417

    Article  Google Scholar 

  18. Liu L, Yang X, Liu H, Wang M, Welles S, Márquez S, Frank A, Haas CN (2016) spatial-temporal analysis of air pollution, climate change, and total mortality in 120 cities of China, 2012–2013. Front Public Health 4:143

    Article  Google Scholar 

  19. Medsker LRR, Jain LC (2001) Recurrent neural networks. Des Appl vol 5

  20. Rahman A, Srikumar V, Smith AD (2018) Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl Energy 212:372–385. https://doi.org/10.1016/j.apenergy.2017.12.051

    Article  Google Scholar 

  21. Vapnik V, Izmailov R (2017) Knowledge transfer in SVM and neural networks. Anna Math Artif Intell 81(1–2):3–19

    Article  MathSciNet  Google Scholar 

  22. Xie Y, Zhao B, Zhang L, Luo R (2015) Spatiotemporal variations of PM2.5 and PM10 concentrations between 31 Chinese cities and their relationships with SO2, NO2, CO and O3. Particuology 20:141–149

    Article  Google Scholar 

  23. Yang CT, Chen CJ, Tsan YT, Liu PY, Chan YW, Chan WC (2018) An implementation of real-time air quality and influenza-like illness data storage and processing platform. Comput Hum Behav. https://doi.org/10.1016/j.chb.2018.10.009

    Article  Google Scholar 

  24. Yang CT, Chen ST, Den W, Wang YT, Kristiani E (2018) Implementation of an intelligent indoor environmental monitoring and management system in cloud. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2018.02.041

    Article  Google Scholar 

  25. Yang C, Chen Y, Chan Y et al (2020) Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources. J Supercomput 76:9303–9329. https://doi.org/10.1007/s11227-020-03182-5

    Article  Google Scholar 

  26. Zaytar MA, El Amrani C (2016) Sequence to sequence weather forecasting with long short term memory recurrent neural networks. Int J Comput Appl 143(11):7–11

    Google Scholar 

  27. Zhang W, Eftychiou A, Vrusias B, Antonopoulos N, Zhang D, Ma M, Oikonomou A, Bessis N, Norrington P, Yue Y, et al. (2014) International journal of grid and high performance computing. International Standard Serial Number

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Acknowledgements

This work was funded by the Ministry of Science and Technology (MOST), Taiwan, under grant number MOST 106-3114-M-029-001-A, MOST 106-2621-M-029-001, MOST 109-2119-M-029-001-A and MOST 109-2221-E-029-020-. In addition, this work was also funded in part by the Taichung Veterans General Hospital (TCVGH), Taiwan, under grant number TCVGH-T1087804 and TCVGH-T1097801.

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Authors and Affiliations

  1. Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung City, 407224, Taiwan (R.O.C.)

    Endah Kristiani & Chin-Yin Huang

  2. Department of Informatics, Krida Wacana Christian University, Jakarta, 11470, Indonesia

    Endah Kristiani

  3. Department of Computer Science, Tunghai University, Taichung City, 407224, Taiwan (R.O.C.)

    Ching-Fang Lee & Chao-Tung Yang

  4. Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec.4, Taiwan Boulevard, Taichung, 407224, Taiwan (R.O.C.)

    Chao-Tung Yang

  5. Department of Emergency Medicine, Taichung Veterans General Hospital, Taichung City, 407204, Taiwan (R.O.C.)

    Yu-Tse Tsan & Wei-Cheng Chan

  6. School of Medicine, Chung Shan Medical University, Taichung City, 40201, Taiwan (R.O.C.)

    Yu-Tse Tsan

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  1. Endah Kristiani
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  2. Ching-Fang Lee
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  3. Chao-Tung Yang
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  4. Chin-Yin Huang
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  5. Yu-Tse Tsan
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  6. Wei-Cheng Chan
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Correspondence to Chao-Tung Yang.

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Kristiani, E., Lee, CF., Yang, CT. et al. Air quality monitoring and analysis with dynamic training using deep learning. J Supercomput 77, 5586–5605 (2021). https://doi.org/10.1007/s11227-020-03492-8

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  • DOI: https://doi.org/10.1007/s11227-020-03492-8

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