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When the quality of water for concrete preparation is uncertain, its impact on the setting time of cement and compressive strength of mortar is assessed by comparison with de-ionized or distilled water benchmarks. American Society for Testing and Materials (ASTM) C1602 requires the setting times to be within 90 minutes of the control, British Standard (BS) 3146:1980 allows a 30-minute variance in the initial setting, while British Standards European Norm (BS EN) 1008 specifies initial setting...
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Predicting 'very poor' beach water quality gradings using classification tree.

Wai Thoe1, King Wah Choi2, Joseph Hun-wei Lee2

  • 1Department of Civil and Environmental Engineering, Environmental and Water Studies, Stanford University, Stanford, CA 94305, USA

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PubMed
Summary

Classification trees improve beach water quality predictions by better identifying high Escherichia coli (E. coli) events. This enhanced accuracy aids in public health protection for Hong Kong beaches.

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Area of Science:

  • Environmental Science
  • Water Quality Management
  • Predictive Modeling

Background:

  • Existing beach water quality prediction systems in Hong Kong utilize multiple linear regression (MLR) models.
  • MLR models struggle to accurately predict infrequent 'very poor' water quality events, defined by Escherichia coli (E. coli) concentrations exceeding 610 counts/100 mL.

Purpose of the Study:

  • To enhance the accuracy of predicting 'very poor' beach water quality events using classification trees.
  • To compare the performance of classification trees against MLR models before and after the Harbour Area Treatment Scheme implementation.
  • To evaluate the effectiveness of binary and multi-category classification trees for predicting E. coli levels.

Main Methods:

  • Development of binary-output classification trees to predict E. coli concentration thresholds.
  • Application of models to data from three Hong Kong beaches affected by point and non-point source pollution.
  • Analysis of model performance across different periods, including pre- and post-Harbour Area Treatment Scheme implementation.

Main Results:

  • Classification trees demonstrated superior performance in capturing 'very poor' E. coli events compared to MLR models, with an average 20% increase in correct positives.
  • Binary classification trees were more effective than four-category trees in predicting 'very poor' events, which tended to generate excessive false alarms.
  • The study identified systematic changes in water quality linked to the Harbour Area Treatment Scheme.

Conclusions:

  • Classification trees offer a significant improvement over MLR for predicting critical 'very poor' beach water quality events.
  • A combined modeling approach integrating MLR and classification trees is recommended to optimize Hong Kong's beach water quality prediction system.
  • The findings provide valuable insights for public health protection and coastal water management strategies.