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Abstract <p>The study aims to improve accuracy and reliability of streamflow forecasting through the optimization of artificial neural networks (ANNs) using three meta-heuristic algorithms: Artificial Rabbits Optimization (ARO), Mayfly Optimization Algorithm (MOA), and Gray Wolf Optimizer (GWO) coupled with Particle Swarm Optimization (PSO) (GWO-PSO). The study was conducted to predict monthly streamflow at the Fermatou and Bou Birek stations in the Soummam watershed, situated in the north of Algeria. Optimal inputs and parameter combinations for the hybrid ANN models were determined using the autocorrelation function (ACF), partial autocorrelation function (PACF), and cross-correlation function (XACF). The numerical results revealed the superior performance of the ANN-ARO, with correlation coefficients <i>R</i>&#xa0;and Nash–Sutcliffe efficiency ranging from 0.981 to 0.982 and from 0.960 to 0.962, respectively, for the two stations. These outcomes surpassed those achieved by the ANN-GWO-PSO and ANN-MOA. It should be pointed out that when comparing the new ARO method to existing employed meta-heuristic algorithms, it showed improved precision in results and prediction accuracy.</p>

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Hybridization of Artificial Neural Networks with Artificial Rabbits Optimization for Improving Monthly Streamflow Forecasting: A Case Study of the Soummam Watershed, Algeria

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • N. Daif,
  • A. Hebal,
  • B. Boucetta

摘要

Abstract

The study aims to improve accuracy and reliability of streamflow forecasting through the optimization of artificial neural networks (ANNs) using three meta-heuristic algorithms: Artificial Rabbits Optimization (ARO), Mayfly Optimization Algorithm (MOA), and Gray Wolf Optimizer (GWO) coupled with Particle Swarm Optimization (PSO) (GWO-PSO). The study was conducted to predict monthly streamflow at the Fermatou and Bou Birek stations in the Soummam watershed, situated in the north of Algeria. Optimal inputs and parameter combinations for the hybrid ANN models were determined using the autocorrelation function (ACF), partial autocorrelation function (PACF), and cross-correlation function (XACF). The numerical results revealed the superior performance of the ANN-ARO, with correlation coefficients R and Nash–Sutcliffe efficiency ranging from 0.981 to 0.982 and from 0.960 to 0.962, respectively, for the two stations. These outcomes surpassed those achieved by the ANN-GWO-PSO and ANN-MOA. It should be pointed out that when comparing the new ARO method to existing employed meta-heuristic algorithms, it showed improved precision in results and prediction accuracy.