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Related Experiment Video

Updated: Aug 11, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model.

Subin Lin1, Jiwoong Kim2, Chuanbo Hua3

  • 1Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea.

Water Research
|February 5, 2023
PubMed
Summary

This study introduces a graph attention multivariate time series forecasting (GAMTF) model for optimizing water treatment coagulant dosage. The advanced deep learning approach accurately predicts coagulant dosage and turbidity, improving water quality management.

Keywords:
Attention-based mechanismBig dataCoagulantDeep learningPrediction modelTime series

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Area of Science:

  • Environmental Science
  • Water Treatment Technologies
  • Artificial Intelligence in Environmental Management

Background:

  • Coagulant dosage determination in water treatment is complex, involving nonlinear data and multiple factors.
  • Existing methods are time-consuming and may not fully account for environmental variables like weather.
  • Accurate prediction of coagulant dosage and water quality is crucial for efficient water treatment.

Purpose of the Study:

  • To develop and evaluate a deep learning approach for determining optimal coagulant dosage and predicting settled water turbidity.
  • To leverage long-term water treatment data, including weather conditions, for enhanced prediction accuracy.
  • To demonstrate the superiority of a novel graph attention multivariate time series forecasting (GAMTF) model over conventional methods.

Main Methods:

  • Development of a graph attention multivariate time series forecasting (GAMTF) model.
  • Utilizing a decade of water treatment data (2011-2021) encompassing various operational and weather parameters.
  • Comparative analysis of the GAMTF model against traditional machine learning and other deep learning models.

Main Results:

  • The GAMTF model achieved a high coefficient of determination (R² = 0.94) and low root-mean-square error (RMSE = 3.55) in predicting coagulant dosage and turbidity.
  • GAMTF significantly outperformed other models, which showed R² values ranging from 0.63 to 0.89 and RMSE values from 4.80 to 38.98.
  • The model successfully predicted both coagulant dosage and settled water turbidity simultaneously, highlighting its ability to capture complex feature interrelationships.

Conclusions:

  • The GAMTF model represents a significant advancement in water treatment decision-support systems.
  • This study marks the first successful application of a multivariate time series deep learning model, specifically a graph attention-based approach, using long-term data for water treatment optimization.
  • The findings suggest that advanced deep learning techniques can effectively improve the accuracy and efficiency of water treatment processes.