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...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-024-00699-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846147432323219456 |
|---|---|
| author | Rana Muhammad Adnan Wang Mo Ahmed A. Ewees Salim Heddam Ozgur Kisi Mohammad Zounemat-Kermani |
| author_facet | Rana Muhammad Adnan Wang Mo Ahmed A. Ewees Salim Heddam Ozgur Kisi Mohammad Zounemat-Kermani |
| author_sort | Rana Muhammad Adnan |
| collection | DOAJ |
| description | 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 Runge–Kutta with Aquila optimizer (LSTM-RUNAO). The key novelty of this research lies in the developing and applying the LSTM-RUNAO model, which combines Runge–Kutta and Aquila optimizer to enhance streamflow prediction accuracy. The models’ performance is compared against the conventional ANN method, analyzing monthly streamflow prediction across three data split scenarios (50–50%, 60–40%, and 75–25%). Results show that the LSTM-RUNAO model outperformed conventional ANN methods, achieving a 28.7% reduction in root mean square error (RMSE) and a 20.3% reduction in mean absolute error (MAE) compared to standard ANN models. In addition, the model yielded a Nash–Sutcliffe Efficiency (NSE) improvement of 12.4% and an R-squared value increase of 7.8%. The study advocates for the 75–25% train-test data splitting scenario for optimal performance in data-driven methodologies. Furthermore, it elucidates the nuanced influence of input variables on prediction accuracy, emphasizing the importance of thoughtful consideration during model development. In summary, this research contributes valuable insights and introduces an innovative hybrid model to enhance the reliability of streamflow prediction models for practical applications. |
| format | Article |
| id | doaj-art-a8fd908529d24f74ab02dce8283879b5 |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-a8fd908529d24f74ab02dce8283879b52024-12-01T12:44:01ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832024-11-0117112310.1007/s44196-024-00699-yEnhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila OptimizerRana Muhammad Adnan0Wang Mo1Ahmed A. Ewees2Salim Heddam3Ozgur Kisi4Mohammad Zounemat-Kermani5College of Architecture and Urban Planning, Guangzhou UniversityCollege of Architecture and Urban Planning, Guangzhou UniversityDepartment of Computer, Damietta UniversityFaculty of Science, Agronomy Department, University of SkikdaDepartment of Civil Engineering, Lübeck University of Applied SciencesDepartment of Water Engineering, Shahid Bahonar University of KermanAbstract 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 Runge–Kutta with Aquila optimizer (LSTM-RUNAO). The key novelty of this research lies in the developing and applying the LSTM-RUNAO model, which combines Runge–Kutta and Aquila optimizer to enhance streamflow prediction accuracy. The models’ performance is compared against the conventional ANN method, analyzing monthly streamflow prediction across three data split scenarios (50–50%, 60–40%, and 75–25%). Results show that the LSTM-RUNAO model outperformed conventional ANN methods, achieving a 28.7% reduction in root mean square error (RMSE) and a 20.3% reduction in mean absolute error (MAE) compared to standard ANN models. In addition, the model yielded a Nash–Sutcliffe Efficiency (NSE) improvement of 12.4% and an R-squared value increase of 7.8%. The study advocates for the 75–25% train-test data splitting scenario for optimal performance in data-driven methodologies. Furthermore, it elucidates the nuanced influence of input variables on prediction accuracy, emphasizing the importance of thoughtful consideration during model development. In summary, this research contributes valuable insights and introduces an innovative hybrid model to enhance the reliability of streamflow prediction models for practical applications.https://doi.org/10.1007/s44196-024-00699-yStreamflow predictionNeural networksData splittingRunge–Kutta with Aquila optimizer |
| spellingShingle | Rana Muhammad Adnan Wang Mo Ahmed A. Ewees Salim Heddam Ozgur Kisi Mohammad Zounemat-Kermani Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer International Journal of Computational Intelligence Systems Streamflow prediction Neural networks Data splitting Runge–Kutta with Aquila optimizer |
| title | Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer |
| title_full | Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer |
| title_fullStr | Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer |
| title_full_unstemmed | Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer |
| title_short | Enhancing Streamflow Prediction Accuracy: A Comprehensive Analysis of Hybrid Neural Network Models with Runge–Kutta with Aquila Optimizer |
| title_sort | enhancing streamflow prediction accuracy a comprehensive analysis of hybrid neural network models with runge kutta with aquila optimizer |
| topic | Streamflow prediction Neural networks Data splitting Runge–Kutta with Aquila optimizer |
| url | https://doi.org/10.1007/s44196-024-00699-y |
| work_keys_str_mv | AT ranamuhammadadnan enhancingstreamflowpredictionaccuracyacomprehensiveanalysisofhybridneuralnetworkmodelswithrungekuttawithaquilaoptimizer AT wangmo enhancingstreamflowpredictionaccuracyacomprehensiveanalysisofhybridneuralnetworkmodelswithrungekuttawithaquilaoptimizer AT ahmedaewees enhancingstreamflowpredictionaccuracyacomprehensiveanalysisofhybridneuralnetworkmodelswithrungekuttawithaquilaoptimizer AT salimheddam enhancingstreamflowpredictionaccuracyacomprehensiveanalysisofhybridneuralnetworkmodelswithrungekuttawithaquilaoptimizer AT ozgurkisi enhancingstreamflowpredictionaccuracyacomprehensiveanalysisofhybridneuralnetworkmodelswithrungekuttawithaquilaoptimizer AT mohammadzounematkermani enhancingstreamflowpredictionaccuracyacomprehensiveanalysisofhybridneuralnetworkmodelswithrungekuttawithaquilaoptimizer |