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Big Data Analytics for Water Resources Sustainability Evaluation

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High-Performance Computing Applications in Numerical Simulation and Edge Computing (HPCMS 2018, HiDEC 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 913))

Abstract

With the advances in remote sensing and computing technology, water resource sustainability evaluation is ingested with high volume data acquired from heterogeneous sources. However, traditional theories and methods for comprehensive water resources sustainability evaluation are challenged by the large quantity, high velocity, and high diversity of those data sets. In this paper, we propose a framework for big data analytics based water resource sustainability evaluation. We build a prototype for regional water resource sustainability evaluation based on big data of regional economic and social development. We build the relationship between economic development and water demand is modeled through regression analysis on water vertical industrial usage distribution, population, and water supply capacity. In our prototype, users can model and predict regional water resource demand and sustainability under constraints of population and industrial development. Results show that the proposed prototype can be used to evaluate regional water resource sustainability and environmental performance in practice and provide scientific basis and guidance to formulate water supply policies.

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Acknowledgments

This research was funded by the Science and Technology Planning Program of Department of Water Resources of Zhejiang Province (Grant No. RC1843).

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Authors and Affiliations

  1. School of Earth Science and Engineering, Hohai University, Nanjing, 210098, Jiangsu, China

    Yinghui Zhao & Ru An

  2. Department of Water Resources, Zhejiang Tongji Vocational College of Science and Technology, Hangzhou, 311231, Zhejiang, China

    Yinghui Zhao

Authors
  1. Yinghui Zhao
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  2. Ru An
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Corresponding author

Correspondence to Ru An .

Editor information

Editors and Affiliations

  1. University of Science and Technology, Beijing, China

    Changjun Hu

  2. China Institute of Atomic Energy, Beijing, China

    Wen Yang

  3. Hangzhou Dianzi University, Hangzhou, China

    Congfeng Jiang

  4. Computer Science, Texas Tech University, Lubbock, USA

    Dong Dai

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Zhao, Y., An, R. (2019). Big Data Analytics for Water Resources Sustainability Evaluation. In: Hu, C., Yang, W., Jiang, C., Dai, D. (eds) High-Performance Computing Applications in Numerical Simulation and Edge Computing. HPCMS HiDEC 2018 2018. Communications in Computer and Information Science, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-32-9987-0_3

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  • DOI: https://doi.org/10.1007/978-981-32-9987-0_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9986-3

  • Online ISBN: 978-981-32-9987-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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