Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer

Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer

Abstract This study investigates the efficacy of hybrid artificial neural network (ANN) methods, incorporating metaheuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), Aquila optimizer (AO), Runge–Kutta (RUN), and the novel ANN-based Rung...

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Bibliographic Details
Main Authors: Rana Muhammad Adnan, Wang Mo, Ahmed A. Ewees, Salim Heddam, Ozgur Kisi, Mohammad Zounemat-Kermani
Format: Article
Language:English
Published: Springer 2024-11-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Streamflow prediction
Neural networks
Data splitting
Runge–Kutta with Aquila optimizer
Online Access:https://doi.org/10.1007/s44196-024-00699-y
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https://doi.org/10.1007/s44196-024-00699-y

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