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A physics-informed statistical learning framework for forecasting local suspended sediment concentrations in marine

Shaotong Zhang1, Jinran Wu2, You-Gan Wang2

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This study introduces a new method for forecasting suspended sediment concentration (SSC) in river deltas. The approach accurately predicts SSC up to six hours ahead using only past SSC data, simplifying water quality monitoring.

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Augmented lncosh ridge regressionMarine ranchingOutlier handlingTemporal auto-correlationThe Yellow River DeltaWater quality

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Area of Science:

  • Environmental Science
  • Hydrology
  • Data Science

Background:

  • Accurate monitoring of water quality, specifically suspended sediment concentration (SSC), is crucial for understanding delta dynamics.
  • Traditional process-based models for SSC prediction often require complex hydrodynamic data, increasing measurement uncertainty and cost.

Purpose of the Study:

  • To develop a data-driven framework for accurate, short-term SSC forecasting in the Yellow River Delta.
  • To create a model that relies solely on SSC data, reducing reliance on concurrent hydrodynamic measurements.

Main Methods:

  • In-situ monitoring of SSC and hydrodynamics in the Yellow River Delta.
  • Empirical mode decomposition and spectral analysis to identify periodicities in SSC variations.
  • A novel augmented lncosh ridge regression incorporating a lncosh function for outlier handling and temporal auto-correlation.

Main Results:

  • The proposed decomposition-ensemble framework achieved high-accuracy 6-hour-ahead SSC forecasting.
  • Mean relative errors ranged from 5.80% to 9.44% in the case study.
  • The model demonstrated superiority over process-based models by requiring only SSC data for prediction.

Conclusions:

  • The developed framework offers a cost-effective and less uncertain alternative for SSC forecasting.
  • This data-driven approach, dependent only on SSC, can be extended to forecast other natural signals with superimposed components of various timescales.