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Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM

Author

Listed:
  • Guoliang Guan

    (Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Yonggui Wang

    (Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Ling Yang

    (Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Jinzhao Yue

    (Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Qiang Li

    (Department of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Jianyun Lin

    (Ningbo Ligong Environment and Energy Technology Co., Ltd., Ningbo 315800, China)

  • Qiang Liu

    (Sichuan Province Environmental Monitoring Station, Chengdu 610091, China)

Abstract

The openly released and measured data from automatic hydrological and water quality stations in China provide strong data support for water environmental protection management and scientific research. However, current public data on hydrology and water quality only provide real-time data through data tables in a shared page. To excavate the supporting effect of these data on water environmental protection, this paper designs a water-quality-prediction and pollution-risk early-warning system. In this system, crawler technology was used for data collection from public real-time data. Additionally, a modified long short-term memory (LSTM) was adopted to predict the water quality and provide an early warning for pollution risks. According to geographic information technology, this system can show the process of spatial and temporal variations of hydrology and water quality in China. At the same time, the current and future water quality of important monitoring sites can be quickly evaluated and predicted, together with the pollution-risk early warning. The data collected and the water-quality-prediction technique in the system can be shared and used for supporting hydrology and in water quality research and management.

Suggested Citation

  • Guoliang Guan & Yonggui Wang & Ling Yang & Jinzhao Yue & Qiang Li & Jianyun Lin & Qiang Liu, 2022. "Water-Quality Assessment and Pollution-Risk Early-Warning System Based on Web Crawler Technology and LSTM," IJERPH, MDPI, vol. 19(18), pages 1-16, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11818-:d:918855
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    References listed on IDEAS

    as
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    5. Feiyang Xia & Dengdeng Jiang & Lingya Kong & Yan Zhou & Jing Wei & Da Ding & Yun Chen & Guoqing Wang & Shaopo Deng, 2022. "Prediction of Dichloroethene Concentration in the Groundwater of a Contaminated Site Using XGBoost and LSTM," IJERPH, MDPI, vol. 19(15), pages 1-24, July.
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