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Forecasting Sensor Data Using Multivariate Time Series Deep Learning

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1306))

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Abstract

Vietnam is a country that has a long coastline, stretching from its North to its South. This has many advantage for aquaculture and fisheries, however, the Global climate change and water pollution have caused problems to the farmers in fish/shrimp raising. Tackling the problem of monitoring and managing quality of the water to help the farmers is very necessary. By monitoring the real-time indicators of salinity, temperature, pH, and dissolved oxygen which are produced by sensor networks, and forecasting them to get early warning, we can help the farmers in shrimp/fish raising. In this work, we propose model for forecasting the water quality indicators by using deep learning (Long-Short Term Memory) with Multivariate Time Series. Experimental results on several data sets show that the proposed approach works well and can be applied to real systems.

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Notes

  1. 1.

    https://www.digitalocean.com.

  2. 2.

    https://www.bco-dmo.org/dataset-deployment/455476.

  3. 3.

    https://data.gov.au/dataset/ds-nsw-c2041218-0496-453a-ac22-9e974fdebf89/distribution/dist-nsw-5154e497-647f-4e2a-a2ad-151fa293b0da/details?q=.

  4. 4.

    https://www.seanoe.org/data/00453/56501.

  5. 5.

    https://colab.research.google.com/.

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Acknowledgments

This study is funded in part by the Can Tho University Improvement Project VN14-P6 supported by a Japanese ODA loan.

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Authors and Affiliations

  1. College of Information and Communication Technology, Can Tho University, Can Tho city, Vietnam

    Nguyen Thai-Nghe & Nguyen Thanh-Hai

Authors
  1. Nguyen Thai-Nghe
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  2. Nguyen Thanh-Hai
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Corresponding author

Correspondence to Nguyen Thai-Nghe .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Sungkyunkwan University, Suwon, Korea (Republic of)

    Tai M. Chung

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Thai-Nghe, N., Thanh-Hai, N. (2020). Forecasting Sensor Data Using Multivariate Time Series Deep Learning. In: Dang, T.K., Küng, J., Takizawa, M., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2020. Communications in Computer and Information Science, vol 1306. Springer, Singapore. https://doi.org/10.1007/978-981-33-4370-2_16

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  • DOI: https://doi.org/10.1007/978-981-33-4370-2_16

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