A probabilistic decision support tool for prediction and management of rainfall-related poor water quality events for a drinking water treatment plant.

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
Read More Add to Saved list
  • 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:
      Drinking Water* ; Water Purification*; Water Quality ; Bayes Theorem ; Australia ; Environmental Monitoring
    • Abstract:
      A data-driven Bayesian Network (BN) model was developed for a large Australian drinking water treatment plant, whose raw water comes from a river into which a number of upstream dams outflow water and smaller tributaries flow. During wet weather events, the spatial distribution of rainfall has a crucial role on the incoming raw water quality, as runoff from specific sub-catchments usually causes significant turbidity and conductivity issues, as opposed to larger dam outflows which have typically better water quality. The BN relies on a conceptual model developed following expert consultation, as well as a combination of different types (e.g. water quality, flow, rainfall) and amount (e.g. high-frequency, daily, scarce depending on variable) of historical data. The validated model proved to have acceptable accuracy in predicting the probability of different incoming raw water quality ranges, and can be used to assess different scenarios (e.g. timing, flow) of dam water releases, for the purpose of achieving dilution of the tributary's poor-quality water and mitigate related drinking water treatment challenges.
      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 © 2023 Elsevier Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: Bayesian networks; Drinking water treatment; Prediction modelling; Water quality
    • Accession Number:
      0 (Drinking Water)
    • Publication Date:
      Date Created: 20230129 Date Completed: 20230303 Latest Revision: 20230303
    • Publication Date:
      20240829
    • Accession Number:
      10.1016/j.jenvman.2022.117209
    • Accession Number:
      36709713