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Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning

Liang Dong1, Jin Zhang2

  • 1College of Life Science and Technology, Jinan University, 510632 Guangzhou, China.

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A new hybrid model accurately predicts polycyclic aromatic hydrocarbons (PAHs) in surface water. This advanced method combines decomposition techniques with deep learning for improved water quality management.

Keywords:
Artificial neural networksHybrid modellingPAHsTwo-stage decompositionWater quality modelling

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Area of Science:

  • Environmental Science
  • Water Resource Management
  • Data Science

Background:

  • Predicting polycyclic aromatic hydrocarbons (PAHs) in surface water is complex due to dynamic processes.
  • Effective surface water management requires accurate PAH concentration forecasting.

Purpose of the Study:

  • To develop a novel hybrid model for predicting PAHs in surface water.
  • To enhance integrated surface water management through improved PAH prediction.

Main Methods:

  • A two-stage decomposition technique using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD).
  • Application of a Long Short-Term Memory (LSTM) deep learning algorithm to analyze decomposed subsequences.
  • Integration of subsequence predictions to generate final PAH forecasts.

Main Results:

  • The CEEMDAN-VMD-LSTM model demonstrated superior performance compared to benchmark data-driven methods.
  • Achieved Mean Absolute Error (MAE) of 27.89, Root Mean Square Error (RMSE) of 37.92, and R-squared (R²) of 0.85.
  • The model effectively captures latent dynamic characteristics for accurate PAH prediction.

Conclusions:

  • The proposed hybrid method offers an effective and accurate approach for water quality prediction.
  • This novel model serves as a valuable tool for integrated surface water management.
  • Combining CEEMDAN, VMD, and LSTM enhances PAH prediction accuracy and reliability.