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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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Related Experiment Videos

Data driven water quality assessment using machine learning and synthetic data generation.

Naga Durga Satya Siva Kiran Relangi1, D Venkata Naga Raju1, P Venkata Rama Raju1

  • 1Department of Information Technology, Shri Vishnu Engineering College for Women (Autonomous), Bhimavaram, India.

Scientific Reports
|June 4, 2026
PubMed
Summary

Generating synthetic water quality data improves machine learning model accuracy for predicting water quality. Oversampling techniques like SMOTE create high-quality synthetic datasets, achieving 99.47% accuracy in water classification tasks.

Keywords:
Extreme gradient boosting (XGB)Gradient boosting (GB)Logistic regression (LR)Random forest (RF)Synthetic minority oversampling technique (SMOTE)Water quality index (WQI)

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Area of Science:

  • Environmental Science
  • Data Science
  • Machine Learning

Background:

  • Ensuring access to clean water is a global challenge, vital for public health.
  • Accurate water quality estimation and classification are complex due to data variability and limited datasets.
  • Existing physical and data-driven models face challenges in predicting water quality effectively.

Purpose of the Study:

  • To develop advanced machine learning models for water quality prediction.
  • To generate synthetic datasets to overcome limitations of real-world water quality data.
  • To enhance the accuracy of water quality classification and prediction models.

Main Methods:

  • Synthetic data generation using oversampling techniques, specifically SMOTE (Synthetic Minority Over-sampling Technique) and GAN (Generative Adversarial Network).
  • Balancing existing water quality datasets to create a more robust training set.
  • Training and evaluating Gradient Boosting (GB) and XGBoost (XGB) machine learning models on the generated synthetic datasets.

Main Results:

  • SMOTE generated a high-quality synthetic Drinking Water Final dataset with a low Maximum Mean Discrepancy (MMD) score of 0.0067.
  • GB and XGB models achieved 99.47% test accuracy on the SMOTE-generated synthetic dataset.
  • While GANs showed potential, SMOTE-generated datasets demonstrated superior quality and model performance for water quality analysis.

Conclusions:

  • Synthetic data generation, particularly via oversampling methods like SMOTE, significantly enhances machine learning model performance in water quality prediction.
  • The study validates the effectiveness of using balanced, synthetic datasets for improving the accuracy of water quality classification and prediction.
  • This approach addresses the challenge of limited data, paving the way for more reliable water quality assessment tools.