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Which riverine water quality parameters can be predicted by meteorologically-driven deep learning?

Sheng Huang1, Yueling Wang2, Jun Xia3

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

The Science of the Total Environment
|June 30, 2024
PubMed
Summary

Deep learning models like GRU effectively predict daily river water quality parameters from meteorological data, offering a promising tool for watershed management, especially in data-scarce regions.

Keywords:
Climate changeCollective predictionDeep learningExtreme valueMeteorological conditionWater quality parameter

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Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Climate change and extreme weather events significantly impact river water quality globally.
  • Deep learning shows potential for river water quality management, but its predictive capabilities driven by meteorological data require further exploration.
  • Understanding the predictability of various water quality parameters using meteorological inputs is crucial for effective management.

Purpose of the Study:

  • To investigate the prediction performance of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for riverine water quality parameters.
  • To assess the effectiveness of meteorologically-driven deep learning models in predicting daily water quality parameters and extreme values.
  • To compare the collective prediction performance of deep learning models for multiple water quality parameters against individual predictions.

Main Methods:

  • Utilized meteorological conditions as input data for deep learning models.
  • Applied RNN, LSTM, and GRU models to predict daily river water quality parameters in the Dahei River basin.
  • Evaluated model performance using determination coefficients and analyzed prediction errors for daily averages and extreme values.

Main Results:

  • Deep learning models (LSTM and GRU) accurately predicted most daily water quality parameters, including water temperature, dissolved oxygen, electrical conductivity, chemical oxygen demand, ammonia nitrogen, total phosphorus, and total nitrogen.
  • Turbidity was not effectively predicted by the models.
  • The GRU model achieved the highest performance with an average determination coefficient of 0.94 and showed a limited error increment (10-40%) for predicting daily extreme values.
  • Collective prediction of multiple water quality parameters outperformed individual parameter prediction.

Conclusions:

  • Meteorologically-driven deep learning models, particularly GRU, demonstrate significant potential for predicting various river water quality parameters at a daily scale.
  • These models offer a robust approach for understanding river water quality dynamics and managing water resources, especially in chemically ungauged areas.
  • The findings support the broader application of deep learning for water quality prediction across diverse watersheds facing climate change impacts.