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Abstract <p>Improving rainfall-runoff (RR) modeling aims to refine streamflow predictions using more accurate data and methods, which is crucial for effective water resource management and reducing flood risks. Machine learning models and conceptual models have been utilized in an attempt to perform rainfall-runoff modeling. This study aims to compare the performance of conceptual models; Génie Rural (GR5J) and Hydrologiska Byråns Vattenbalansavdelning (HBV), machine learning models Perceptron; Neural Network (MLPNN), Random Forest Regression (RFR), and hybrid machine learning (ML) models based on Variational mode decomposition (VMD), VMD-MLPNN, and VMD-RFR for rainfall–runoff modelling of watershed in north-central of Algeria. It was obtained that the performance of the hybrid ML models VMD-MLPNN is better than conceptual models and stand-alone machine learning models, with the highest values for correlation coefficient (<i>R</i>) and Nash-Sutcliffe Efficiency (NSE) of approximately 0.990 and 0.964, respectively.</p>

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Comparative Performance of Rainfall-Runoff Models: Conceptual, Machine Learning, and Hybrid Approaches, Case Study of Côtiers Algérois Watershed, Algeria

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Noureddine Daif,
  • Aziz Hebal,
  • Brahim Boucetta,
  • Asma Metarah

摘要

Abstract

Improving rainfall-runoff (RR) modeling aims to refine streamflow predictions using more accurate data and methods, which is crucial for effective water resource management and reducing flood risks. Machine learning models and conceptual models have been utilized in an attempt to perform rainfall-runoff modeling. This study aims to compare the performance of conceptual models; Génie Rural (GR5J) and Hydrologiska Byråns Vattenbalansavdelning (HBV), machine learning models Perceptron; Neural Network (MLPNN), Random Forest Regression (RFR), and hybrid machine learning (ML) models based on Variational mode decomposition (VMD), VMD-MLPNN, and VMD-RFR for rainfall–runoff modelling of watershed in north-central of Algeria. It was obtained that the performance of the hybrid ML models VMD-MLPNN is better than conceptual models and stand-alone machine learning models, with the highest values for correlation coefficient (R) and Nash-Sutcliffe Efficiency (NSE) of approximately 0.990 and 0.964, respectively.