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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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Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods.

Xianhe Wang1,2, Ying Li1,2, Qian Qiao1

  • 1School of Applied Chemistry and Materials, Zhuhai College of Science and Technology, Zhuhai 519041, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

Accurate water quality prediction is crucial for environmental protection. This study introduces a novel feature selection method and evaluates machine learning models, finding Long Short-Term Memory (LSTM) networks highly effective for time-series water quality forecasting.

Keywords:
LSTMcomprehensive weight-based approachfeature selectionmachine learningwater quality prediction

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Global environmental concerns necessitate robust water resource preservation and ecological balance.
  • Accurate monitoring and prediction of water quality are vital for environmental protection.
  • Existing water quality prediction methods face challenges in accuracy and reliability.

Purpose of the Study:

  • To develop a comprehensive weight-based approach for selecting crucial features in water quality prediction.
  • To evaluate the performance of various machine learning models for water quality prediction.
  • To identify the most effective models for accurate and robust water quality forecasting.

Main Methods:

  • A novel feature selection approach combining entropy weighting and Pearson correlation coefficient was developed.
  • Multiple machine learning models, including Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM), were explored.
  • Model performance was evaluated based on prediction accuracy and robustness for various water quality parameters.

Main Results:

  • The combined weighting approach effectively selected features based on correlation and information content, reducing bias.
  • Support Vector Machines (SVM) showed strong performance in predicting Dissolved Oxygen (DO).
  • Multilayer Perceptron (MLP) excelled in nonlinear modeling for multiple water quality parameters.
  • Random Forest (RF) and XGBoost exhibited comparatively lower performance.
  • Long Short-Term Memory (LSTM) networks demonstrated exceptional accuracy and stability in capturing dynamic patterns for time-series water quality prediction.

Conclusions:

  • The proposed comprehensive feature selection method enhances the accuracy and robustness of water quality prediction.
  • Long Short-Term Memory (LSTM) networks are highly effective for time-series water quality prediction due to their ability to capture dynamic patterns.
  • Machine learning models offer promising solutions for improving water quality monitoring and management.