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Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based

Banu Yilmaz1, Egemen Aras1, Sinan Nacar2

  • 1Karadeniz Technical University, Faculty of Technology, Department of Civil Engineering, Trabzon, Turkey.

The Science of the Total Environment
|May 27, 2018
PubMed
Summary

Estimating river sediment load is crucial for dam lifespan prediction. Multivariate adaptive regression splines (MARS) proved most accurate for predicting suspended sediment load (SSL) using streamflow data.

Keywords:
Heuristic regressionOptimization algorithmReservoir lifeÇoruh River Basin

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Area of Science:

  • Hydrology
  • Environmental Engineering
  • Water Resource Management

Background:

  • Dam functionality is significantly impacted by reservoir sediment accumulation.
  • Accurate estimation of riverine sediment load is vital for dam design and lifespan prediction.
  • Direct sediment measurement is costly and often impractical at all monitoring stations.

Purpose of the Study:

  • To evaluate and compare various regression models for estimating suspended sediment load (SSL).
  • To identify the most accurate predictive model for SSL at gauging stations on the Çoruh River, Turkey.
  • To assess the efficacy of streamflow data in conjunction with SSL prediction models.

Main Methods:

  • Comparison of classical regression analysis (CRA) with advanced algorithms: artificial bee colony (ABC), teaching-learning-based optimization (TLBO), and multivariate adaptive regression splines (MARS).
  • Utilized streamflow and historical SSL data as inputs for model training and testing.
  • Employed two distinct training and testing dataset configurations to validate model performance.

Main Results:

  • The MARS model demonstrated superior accuracy, with root mean square error (RMSE) values ranging from 35% to 39% across the two gauging stations.
  • Further refinement using a different dataset configuration yielded significantly lower error rates for MARS, between 7% and 15%.
  • Simultaneous measurement of streamflow and SSL was identified as the most effective parameter for accurate predictive modeling.

Conclusions:

  • Multivariate adaptive regression splines (MARS) is the most accurate and reliable model for predicting suspended sediment load (SSL).
  • Integrating streamflow data with SSL measurements significantly enhances the predictive capability of hydrological models.
  • The findings provide valuable insights for optimizing dam management and infrastructure planning through improved sediment load forecasting.