IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i18p11818-d918855.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/18/11818/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/18/11818/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. Pavai & T. V. Geetha, 2017. "Improving the freshness of the search engines by a probabilistic approach based incremental crawler," Information Systems Frontiers, Springer, vol. 19(5), pages 1013-1028, October.
    2. Jingya Ban & Bing Ling & Wei Huang & Xiaobo Liu & Wenqi Peng & Jianmin Zhang, 2021. "Spatiotemporal Variations in Water Flow and Quality in the Sanyang Wetland, China: Implications for Environmental Restoration," Sustainability, MDPI, vol. 13(9), pages 1-16, April.
    3. 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.
    4. Chimmula, Vinay Kumar Reddy & Zhang, Lei, 2020. "Time series forecasting of COVID-19 transmission in Canada using LSTM networks," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    5. Zhen Wang & Hongyan Ren & An Zhang & Dafang Zhuang, 2021. "Spatiotemporal Hotspots of Study Areas in Research of Gastric Cancer in China Based on Web-Crawled Literature," IJERPH, MDPI, vol. 18(8), pages 1-14, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Peipei & Zheng, Xinqi & Ai, Gang & Liu, Dongya & Zhu, Bangren, 2020. "Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Yulan Li & Kun Ma, 2022. "A Hybrid Model Based on Improved Transformer and Graph Convolutional Network for COVID-19 Forecasting," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    3. Zhao, Xinxing & Li, Kainan & Ang, Candice Ke En & Cheong, Kang Hao, 2023. "A deep learning based hybrid architecture for weekly dengue incidences forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    4. Yunhan Huang & Quanyan Zhu, 2022. "Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review," Dynamic Games and Applications, Springer, vol. 12(1), pages 7-48, March.
    5. Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    6. Zahra Dehghan Shabani & Rouhollah Shahnazi, 2020. "Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1005-1025, December.
    7. Crokidakis, Nuno, 2020. "COVID-19 spreading in Rio de Janeiro, Brazil: Do the policies of social isolation really work?," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    8. Bowen Long & Fangya Tan & Mark Newman, 2023. "Forecasting the Monkeypox Outbreak Using ARIMA, Prophet, NeuralProphet, and LSTM Models in the United States," Forecasting, MDPI, vol. 5(1), pages 1-11, January.
    9. Prasanth, Sikakollu & Singh, Uttam & Kumar, Arun & Tikkiwal, Vinay Anand & Chong, Peter H.J., 2021. "Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    10. Hu, Yuntong & Xiao, Fuyuan, 2022. "A novel method for forecasting time series based on directed visibility graph and improved random walk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    11. Kun Zhang & Xing Huo & Kun Shao, 2023. "Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    12. Shalini Shekhawat & Akash Saxena & Ramadan A. Zeineldin & Ali Wagdy Mohamed, 2023. "Prediction of Infectious Disease to Reduce the Computation Stress on Medical and Health Care Facilitators," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    13. Khan, Firdos & Saeed, Alia & Ali, Shaukat, 2020. "Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    14. Lalmuanawma, Samuel & Hussain, Jamal & Chhakchhuak, Lalrinfela, 2020. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    15. Yi-Tui Chen & Yung-Feng Yen & Shih-Heng Yu & Emily Chia-Yu Su, 2020. "An Examination on the Transmission of COVID-19 and the Effect of Response Strategies: A Comparative Analysis," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    16. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    17. Shahid, Farah & Zameer, Aneela & Muneeb, Muhammad, 2020. "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    18. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    19. Nikola Anđelić & Sandi Baressi Šegota & Ivan Lorencin & Zdravko Jurilj & Tijana Šušteršič & Anđela Blagojević & Alen Protić & Tomislav Ćabov & Nenad Filipović & Zlatan Car, 2021. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm," IJERPH, MDPI, vol. 18(3), pages 1-26, January.
    20. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11818-:d:918855. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.