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Enhanced water quality prediction model using advanced hybridized resampling alternating tree-based and deep learning

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  • 1Canadian Centre for Climate Change and Adaptation, University of Prince Edward Island, Charlottetown, PEI, Canada.

Environmental Science and Pollution Research International
|February 24, 2025
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Summary

This study introduces advanced deep learning models, including bidirectional-LSTM (Bi-LSTM), to accurately predict river water quality parameters like turbidity and dissolved oxygen. The Bi-LSTM models demonstrated superior performance in forecasting water quality, offering a valuable tool for environmental management.

Keywords:
Decision treeDeep learningDissolved oxygenMachine learningTurbidityWater quality

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Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Accurate water quality modeling is essential for managing riverine systems and mitigating pollution.
  • Predicting water quality is challenging due to complex interactions between human activities and natural processes.

Purpose of the Study:

  • To develop and evaluate novel deep learning (DL) models for predicting daily turbidity (TU) and dissolved oxygen (DO) in river systems.
  • To compare the performance of bidirectional-LSTM (Bi-LSTM) and bootstrap aggregating with alternating model tree (BA_AMT) models.
  • To identify optimal input configurations for water quality prediction.

Main Methods:

  • Developed hybrid DL models: Bi-LSTM and BA_AMT.
  • Applied models to the Clackamas River, USA, using daily records of water discharge (Q), gage height (GH), water temperature (Tw), specific conductance (SC), and pH.
  • Evaluated model performance using metrics like RMSE, NSE, PBIAS, and RSR under various input scenarios.

Main Results:

  • Bi-LSTM models achieved superior predictive accuracy for both TU and DO compared to BA_AMT.
  • Sensitivity analysis identified key influential parameters: Q and GH for TU; Tw, SC, GH, PH, and Q for DO.
  • BA_AMT models showed a stronger ability to capture extreme water quality values.

Conclusions:

  • Bi-LSTM models offer a highly accurate and scalable approach for water quality forecasting in freshwater systems.
  • Optimizing DL models with metaheuristic techniques can further enhance predictive capabilities.
  • The proposed framework provides a valuable tool for informed environmental management and decision-making.