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Accurate discharge and water level forecasting using ensemble learning with genetic algorithm and singular spectrum

Anh Duy Nguyen1, Phi Le Nguyen2, Viet Hung Vu1

  • 1School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam.

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This study introduces an advanced deep learning model for accurate river discharge (Q) and water level (H) forecasting. The novel approach enhances prediction accuracy by combining ensemble learning, data denoising with Singular Spectrum Analysis (SSA), and hyper-parameter optimization using the Genetic Algorithm (GA).

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Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Accurate forecasting of river discharge (Q) and water level (H) is crucial for hydrological research and flood prediction.
  • Deep learning models show promise for capturing complex, non-linear hydrological data but face challenges with data scarcity, noise, and hyper-parameter tuning.

Purpose of the Study:

  • To develop a novel deep learning-based model for enhanced Q and H prediction.
  • To address limitations of existing methods, including insufficient training data, data noise, and difficulties in hyper-parameter optimization.

Main Methods:

  • An ensemble learning architecture combining multiple deep learning techniques was designed to improve prediction accuracy and address data scarcity.
  • Singular Spectrum Analysis (SSA) was employed for noise and outlier removal from hydrological data.
  • The Genetic Algorithm (GA) was utilized to develop a mechanism for automatic optimization of the prediction model's hyper-parameters.

Main Results:

  • The proposed model demonstrated superior performance compared to existing techniques on datasets from Vietnam's Red and Dakbla rivers, showing significant improvements in NSE, MSE, MAE, and MAPE.
  • Ensemble learning improved the Nash-Sutcliffe Efficiency (NSE) by at least [Formula: see text].
  • SSA-based data preprocessing further enhanced NSE by over [Formula: see text], and GA-based optimization boosted NSE by [Formula: see text] to [Formula: see text].

Conclusions:

  • The novel deep learning model effectively overcomes data scarcity, noise, and hyper-parameter challenges in Q and H forecasting.
  • The integrated approach of ensemble learning, SSA, and GA offers a robust and accurate solution for hydrological predictions.
  • The model's performance validates its potential for practical applications in water resource management and flood risk assessment.