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Open Innovation Web-Based Platform for Evaluation of Water Quality Based on Big Data Analysis

Author

Listed:
  • Xiaofang Han

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China
    School of Environment and Health, Jianghan University, Wuhan 430056, China)

  • Hong Shen

    (Donghu Experimental Station of Lake Ecosystems, Cern Sub-Center of Aquatic Ecosystems, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China)

  • Hongqing Hu

    (College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China)

  • Jerry Gao

    (Computer Engineering Department, San Jose State University, San Jose, CA 95192, USA)

Abstract

There are many models presented that assess water quality. However, the applications of the models are limited due to the difficulty of preparing input data and interpreting model output. In this paper, we developed a Web-based platform to assist researchers in analyzing water quality. The data from sensors can be automatically imported to the platform according to the configured information of data structures. The platform also provides conventional methods and big data methods for the users to analyze water quality. Moreover, the users can choose the water quality parameters according to the water usage. The presented platform can show the model output in a text format and a graphic format, which allows for the analysis to be better understood by the user. The platform integrates the input, analysis, and output together well and brings great convenience to the research on water quality.

Suggested Citation

  • Xiaofang Han & Hong Shen & Hongqing Hu & Jerry Gao, 2022. "Open Innovation Web-Based Platform for Evaluation of Water Quality Based on Big Data Analysis," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:14:p:8811-:d:865994
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    References listed on IDEAS

    as
    1. Fereshteh Modaresi & Shahab Araghinejad, 2014. "A Comparative Assessment of Support Vector Machines, Probabilistic Neural Networks, and K-Nearest Neighbor Algorithms for Water Quality Classification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(12), pages 4095-4111, September.
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