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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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Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation.

Tao Yan1, Annan Zhou2, Shui-Long Shen3

  • 1MOE Key Laboratory of Intelligent Manufacturing Technology, Department of Civil and Environmental Engineering, College of Engineering, Shantou University, Shantou, Guangdong, 515063, China; Discipline of Civil and Infrastructure Engineering, School of Engineering, Royal Melbourne Institute of Technology (RMIT), Victoria, 3001, Australia.

Environmental Pollution (Barking, Essex : 1987)
|December 16, 2022
PubMed
Summary

This study introduces a novel framework using Bayesian-optimised machine learning to predict long-term water quality. The model accurately identified key pollution indicators like total phosphorus (TOP) and chemical oxygen demand (COD) showing upward trends.

Keywords:
Bayesian optimisationKey pollution indicatorsMachine learningTrend analysisWater quality prediction

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Machine Learning Applications

Background:

  • Accurate water quality assessment is crucial for societal well-being.
  • Predicting long-term water quality trends is essential for effective management and pollution control.
  • Existing methods may lack the precision needed for complex estuarine environments.

Purpose of the Study:

  • To propose a new framework for predicting long-term water quality.
  • To utilize Bayesian-optimised machine learning for enhanced prediction accuracy.
  • To identify key pollution indicators and their trends in the Pearl River Estuary.

Main Methods:

  • Development and application of a Bayesian-optimised stacked generalisation (SG-op) model.
  • Utilisation of monitoring data on key pollution indicators from the Pearl River Estuary.
  • Employing the Spearman rank correlation coefficient to analyse indicator variation trends.

Main Results:

  • The SG-op model demonstrated superior performance with high accuracy (0.992) and Kappa coefficient (0.987).
  • Feature importance analysis confirmed the relevance of key pollution indicators in the prediction model.
  • Significant upward trends were observed for total phosphorus (TOP), dissolved oxygen (DO), chemical oxygen demand (COD), and petroleum (PET).

Conclusions:

  • The proposed Bayesian-optimised machine learning framework provides an efficient method for long-term water quality prediction.
  • The findings highlight critical pollution trends in the Pearl River Estuary, requiring attention.
  • This framework offers valuable technical support for water quality management and emergency pollution control strategies.