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International Conference on 5G for Future Wireless Networks

5GWN 2017: 5G for Future Wireless Networks pp 79–88Cite as

SWAT Hydrological Model and Big Data Techniques

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Abstract

Meteorological and hydrological data includes a wide range of items with complex format and large scale. In the era of big data, it brings great opportunities and challenges to meteorology and hydrology services. Because isolated information resources and industry data barriers bring incomplete data parameters, which makes the hydrological model simulate inaccurately. This paper mainly discusses the application of big data technology in Hydro-Meteorological industry. Firstly, it introduces the background and principal of hydrological SWAT model. Secondly, this paper proceeds the SWAT simulation and estimates runoff prediction of WangMo river in GuiZhou province, and analyzes simulation results. Finally, it proposes a big data platform architecture design combines with SWAT hydrological model as future research direction. Big data platform will provide libraries of integrated model, method, component, knowledge database for Hydro-Meteorological resources management. It also offers decision-making for flood control, water shortage, water pollution incidents.

This project has been funded with support by 2017 Youth Technology Foundation of GuiZhou Provincial Meteorological Administration.

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Authors and Affiliations

  1. GuiZhou Provincial Meteorological Administration Information Center, GuiYang, GuiZhou, China

    Xi Guo, Biao Wang, Wei Xiong & Shisheng Jin

Authors
  1. Xi Guo
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  2. Biao Wang
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  3. Wei Xiong
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  4. Shisheng Jin
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Corresponding author

Correspondence to Xi Guo .

Editor information

Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Keping Long

  2. The University of British Columbia, Vancouver, British Columbia, Canada

    Victor C.M. Leung

  3. University of Science and Technology, Beijing, China

    Haijun Zhang

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Zhiyong Feng

  5. Centre of Excellence in Telecommunications, The University of Sydney , Maze Crescent, Australia

    Yonghui Li

  6. University of Science and Technology, Beijing, China

    Zhongshan Zhang

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cite this paper

Guo, X., Wang, B., Xiong, W., Jin, S. (2018). SWAT Hydrological Model and Big Data Techniques. In: Long, K., Leung, V., Zhang, H., Feng, Z., Li, Y., Zhang, Z. (eds) 5G for Future Wireless Networks. 5GWN 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-319-72823-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-72823-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72822-3

  • Online ISBN: 978-3-319-72823-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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