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Approximation in scour depth around spur dikes using novel hybrid ensemble data-driven model.

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  • 1Civil Engineering Department, Delhi Technological University, Delhi 110042, India

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This study presents a hybrid machine learning model to accurately predict scour depth near spur dikes, enhancing riverbank stability assessments. The developed model, Bagging-Additive Regression-Random Tree (B-AR-RT), offers a reliable solution for complex scour phenomena.

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Area of Science:

  • River engineering
  • Hydraulic structures
  • Computational fluid dynamics

Background:

  • Scouring near spur dikes threatens riverbank stability.
  • Accurate prediction of maximum scour depth is critical for river engineering.
  • Scour phenomena around spur dikes are complex and challenging to model.

Purpose of the Study:

  • To develop a reliable ensemble data-driven model for predicting scour depths around spur dikes.
  • To hybridize random tree (RT) with additive regression (AR), bagging (B), and random subspace (RSS) for enhanced prediction accuracy.
  • To identify the most influential parameters affecting scour depth prediction.

Main Methods:

  • Collected a database of 154 experimental observations from existing literature.
  • Performed dimensionless analysis, selecting four input variables (v/vs, y/l, l/d50, Fd50) and one response variable (ds/l).
  • Developed and compared hybrid models, including Bagging-Additive Regression-Random Tree (B-AR-RT), for scour depth prediction.

Main Results:

  • The B-AR-RT model achieved a high coefficient of determination (R2) of 0.9693.
  • The model demonstrated a low root mean square error (RMSE) of 0.1305.
  • Nash-Sutcliffe efficiency (NSE) reached 0.9692, indicating excellent performance.

Conclusions:

  • The developed B-AR-RT model provides a reliable method for predicting scour depths around spur dikes.
  • The hybrid ensemble approach significantly improves prediction accuracy compared to previous methods.
  • Sensitivity analysis identified key parameters influencing scour depth, aiding in engineering design.