Integrating deep learning techniques for effective river water quality monitoring and management.

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  • Additional Information
    • Source:
      Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE
    • Publication Information:
      Original Publication: London ; New York, Academic Press.
    • Subject Terms:
      Water Quality* ; Rivers* ; Deep Learning* ; Environmental Monitoring*/methods; Neural Networks, Computer
    • Abstract:
      Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality. The monitoring system was meticulously set up with strategically positioned sensors at various river locations, ensuring data collection at regular 5-min intervals. This data was then transmitted to a cloud-based web portal, facilitating storage and analysis. To assess water quality, we introduced a novel hybrid approach, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed CNN-LSTM model achieved a validation accuracy of 98.40%, surpassing the performance of other state-of-the-art methods. Notably, the practical application of this system includes real-time alerts, promptly notifying stakeholders when water quality parameters exceed predefined thresholds. This feature aids in making informed decisions in water resource management. The study's contributions lie in its effective river water quality monitoring system, which encompassing various parameters, and its potential to positively impact environmental conservation efforts by providing a valuable tool for informed decision-making and timely interventions.
      Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
      (Copyright © 2024 Elsevier Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: Artificial intelligence (AI); Convolutional neural network; Industrial waste; LSTM network.; River water; Water quality monitoring
    • Publication Date:
      Date Created: 20240920 Date Completed: 20241116 Latest Revision: 20241116
    • Publication Date:
      20241118
    • Accession Number:
      10.1016/j.jenvman.2024.122477
    • Accession Number:
      39303600