Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

• Published in: Stochastic environmental research and risk assessment Vol. 38; no. 10; pp. 3907 - 3927
• Main Authors: Katipoğlu, Okan Mert, Kartal, Veysi, Pande, Chaitanya Baliram
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Aquatic Pollution; Artificial neural networks; Biomimetics; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Dam construction; Earth and Environmental Science; Earth Sciences; Environment; Estimation; Fluid flow; Forecasting; Heuristic methods; Hydraulics; Math. Appl. in Environmental Science; Neural networks; Optimization; Optimization techniques; Original Paper; Physics; Probability Theory and Stochastic Processes; Reservoir management; Rivers; Search algorithms; Sediment load; Sediments; Service life; Statistical analysis; Statistical models; Statistics for Engineering; Stream discharge; Stream flow; Swarm intelligence; Waste Water Technology; Water Management; Water Pollution Control; Water resources; Water resources management; Water utilities; Waterworks; ANN; Firefly optimization; Sediment load; Çoruh river; Artificial bee colony optimization
• ISSN: 1436-3240; 1436-3259; 1436-3259
• DOI: 10.1007/s00477-024-02785-1

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 Çoruh 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 (R 2  = 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.