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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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Suspended sediment load prediction based on soft computing models and Black Widow Optimization Algorithm using an

Fatemeh Panahi1, Mohammad Ehteram2, Mohammad Emami3

  • 1Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran.

Environmental Science and Pollution Research International
|April 27, 2021
PubMed
Summary

Accurate suspended sediment load (SSL) prediction is crucial for water engineering. This study enhanced adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) models using the black widow optimization algorithm (BWOA) for precise daily SSL estimation.

Keywords:
ANFISBlack Widow Optimization AlgorithmSVMSuspended sediment load

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Area of Science:

  • Water Engineering
  • Environmental Science
  • Computational Intelligence

Background:

  • Suspended sediment load (SSL) prediction is a critical challenge in water resource management.
  • Accurate SSL estimation is vital for infrastructure design, ecological health, and water quality monitoring.
  • Existing models often require enhancement to improve prediction accuracy under varying hydrological conditions.

Purpose of the Study:

  • To develop and evaluate advanced computational models for predicting daily suspended sediment load (SSL) in the Telar and Kasilian Rivers.
  • To enhance the performance of adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) models using a novel optimization algorithm.
  • To compare the predictive capabilities of the proposed hybrid models against other established optimization techniques.

Main Methods:

  • Utilized adaptive neuro-fuzzy interface system (ANFIS) and support vector machine (SVM) for SSL estimation.
  • Employed the black widow optimization algorithm (BWOA) to optimize ANFIS and SVM model parameters.
  • Incorporated lagged rainfall, temperature, discharge, and SSL as input variables, with input selection guided by a hybrid Gamma test.
  • Benchmarked ANFIS-BWOA and SVM-BWOA against ANFIS-bat algorithm (BA), SVM-BA, and SVM-particle swarm optimization (PSO) models.

Main Results:

  • The ANFIS-BWOA model demonstrated superior performance, achieving lower Mean Absolute Error (MAE) compared to ANFIS-BA, ANFIS-PSO, and standalone ANFIS models during training for the Telar River.
  • ANFIS-BWOA exhibited the highest coefficient of determination (R 2) among the evaluated models for the Telar River.
  • For the Kasilian River testing phase, ANFIS-BWOA achieved the lowest MAE (899.12 Ton/day) compared to SVM-BWOA, SVM-PSO, SVM-BA, and SVM models.
  • Uncertainty analysis indicated that input uncertainty had a greater impact on model accuracy than parameter uncertainty.

Conclusions:

  • The black widow optimization algorithm (BWOA) significantly enhances the predictive accuracy of ANFIS and SVM models for suspended sediment load (SSL).
  • Hybrid models like ANFIS-BWOA offer a robust approach for precise daily SSL estimation in river systems.
  • Input data uncertainty is a critical factor influencing the reliability of SSL prediction models.