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Predicting river dissolved oxygen time series based on stand-alone models and hybrid wavelet-based models.

Chuang Xu1, Xiaohong Chen1, Lilan Zhang1

  • 1Center for Water Resources and Environment Research, School of Civil Engineering, Sun Yat-sen University, Guangzhou, China.

Journal of Environmental Management
|June 20, 2021
PubMed
Summary

Accurate dissolved oxygen (DO) prediction using machine learning models, including wavelet transform hybrids, improves water resource management. The multicomponent wavelet framework enhanced prediction accuracy for DO time series analysis.

Keywords:
Dissolved oxygen predictionDongjiang river basinHybrid modelsStand-alone modelsWavelet transform

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Accurate dissolved oxygen (DO) prediction is crucial for effective water environment improvement and water resource management.
  • Time series forecasting of DO is complex due to various influencing factors.

Purpose of the Study:

  • To evaluate the performance of stand-alone and hybrid models for daily dissolved oxygen (DO) prediction.
  • To compare different wavelet transform frameworks for hybrid modeling.
  • To identify optimal explanatory variables for DO prediction.

Main Methods:

  • Employed four stand-alone models: multiple linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), and random forest (RF).
  • Developed four hybrid models using wavelet transform (WT) with the MLR, SVM, ANN, and RF models.
  • Utilized maximal information coefficient (MIC) to select optimal explanatory variables and a 5-fold cross-validation grid search for parameter optimization.
  • Investigated two WT frameworks: direct and multicomponent.

Main Results:

  • Maximal information coefficient identified previous DO, water temperature, air temperature, and air pressure as optimal predictors.
  • Prediction accuracy decreased with lead times from 1 to 5 days.
  • Among stand-alone models, MLR showed the best performance (NSE: 0.616-0.921).
  • Hybrid models WT-ANN and WT-MLR demonstrated superior performance.
  • The multicomponent WT framework consistently outperformed the direct framework in hybrid models.

Conclusions:

  • The multicomponent wavelet transform framework generally enhances the predictive accuracy of stand-alone models for DO time series.
  • Hybrid models, particularly WT-ANN and WT-MLR with the multicomponent framework, offer robust solutions for DO prediction in river basins.
  • The study highlights the effectiveness of advanced modeling techniques for water quality forecasting and management.