Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River
The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee...
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Springer Science and Business Media Deutschland GmbH
2025
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| author | Katipo?lu O.M. Kartal V. Pande C.B. |
| author2 | 57203751801 |
| author_facet | 57203751801 Katipo?lu O.M. Kartal V. Pande C.B. |
| author_sort | Katipo?lu O.M. |
| building | UNITEN Library |
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| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t ? 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = ? 251.090, Bias Factor = ? 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources. ? The Author(s) 2024. |
| format | Article |
| id | my.uniten.dspace-36294 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| record_format | dspace |
| spelling | my.uniten.dspace-362942025-03-03T15:41:50Z Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River Katipo?lu O.M. Kartal V. Pande C.B. 57203751801 57221197958 57193547008 Coruh River Bioluminescence Biomimetics Forecasting Neural networks Reservoir management Reservoirs (water) Rivers Sediments Water management Artificial bee colony optimizations Artificial bees Artificial neural network algorithm Coruh river Firefly algorithms Firefly optimization Load values Optimisations Optimization techniques Sediment loads algorithm artificial neural network biotechnology forecasting method optimization sediment transport Optimization The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t ? 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = ? 251.090, Bias Factor = ? 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources. ? The Author(s) 2024. Final 2025-03-03T07:41:50Z 2025-03-03T07:41:50Z 2024 Article 10.1007/s00477-024-02785-1 2-s2.0-85199355668 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199355668&doi=10.1007%2fs00477-024-02785-1&partnerID=40&md5=f0d9592dec2b433465e2d89c26991b95 https://irepository.uniten.edu.my/handle/123456789/36294 38 10 3907 3927 Springer Science and Business Media Deutschland GmbH Scopus |
| spellingShingle | Coruh River Bioluminescence Biomimetics Forecasting Neural networks Reservoir management Reservoirs (water) Rivers Sediments Water management Artificial bee colony optimizations Artificial bees Artificial neural network algorithm Coruh river Firefly algorithms Firefly optimization Load values Optimisations Optimization techniques Sediment loads algorithm artificial neural network biotechnology forecasting method optimization sediment transport Optimization Katipo?lu O.M. Kartal V. Pande C.B. Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River |
| title | Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River |
| title_full | Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River |
| title_fullStr | Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River |
| title_full_unstemmed | Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River |
| title_short | Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River |
| title_sort | sediment load forecasting from a biomimetic optimization perspective: firefly and artificial bee colony algorithms empowered neural network modeling in �oruh river |
| topic | Coruh River Bioluminescence Biomimetics Forecasting Neural networks Reservoir management Reservoirs (water) Rivers Sediments Water management Artificial bee colony optimizations Artificial bees Artificial neural network algorithm Coruh river Firefly algorithms Firefly optimization Load values Optimisations Optimization techniques Sediment loads algorithm artificial neural network biotechnology forecasting method optimization sediment transport Optimization |
| url_provider | http://dspace.uniten.edu.my/ |