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A new framework for water quality forecasting coupling causal inference, time-frequency analysis and uncertainty

Chi Zhang1, Xizhi Nong2, Kourosh Behzadian3

  • 1State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, 430072, China.

Journal of Environmental Management
|November 26, 2023
PubMed
Summary

This study introduces a novel framework for forecasting water quality, combining deep learning with causal inference and uncertainty quantification. This approach significantly reduces errors in total nitrogen predictions and enhances forecast reliability for water resource management.

Keywords:
Causal inferenceCopula functionDeep learning algorithmsTime-series forecastingWater resources management

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Area of Science:

  • Environmental Science
  • Data Science
  • Water Resource Management

Background:

  • Accurate water quality forecasting is vital for environmental management.
  • Traditional data-driven models struggle with complex water quality dynamics.

Purpose of the Study:

  • To develop a holistic framework for time-series forecasting of water quality parameters.
  • To improve the accuracy and reliability of total nitrogen (TN) forecasts in river systems.

Main Methods:

  • Integration of Long Short-Term Memory (LSTM) and Informer deep learning models.
  • Application of causal inference and wavelet decomposition for data pre-processing.
  • Utilizing Copula functions and Bayesian theory for uncertainty quantification.

Main Results:

  • Pre-processing techniques significantly enhanced deep learning model performance.
  • Wavelet-coupled models reduced TN forecasting errors by up to 41.26%.
  • The 95% forecast confidence interval demonstrated high prediction reliability and robustness.

Conclusions:

  • The proposed framework offers a practical methodological approach for water quality forecasting.
  • Advanced data-driven methods, including deep learning and causal inference, are effective for time-series analysis.
  • The study provides valuable references for water resource management and similar environmental projects.