Suspended sediment load prediction in river systems via shuffled frog-leaping algorithm and neural network

• Published in: Earth science informatics Vol. 17; no. 4; pp. 3623 - 3649
• Main Authors: Katipoğlu, Okan Mert, Aktürk, Gaye, Kılınç, Hüseyin Çağan, Terzioğlu, Zeynep Özge, Keblouti, Mehdi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024; Springer Nature B.V
• Subjects: Algorithms; Amphibians; Artificial neural networks; Bias; Disaster risk; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Genetic algorithms; Information Systems Applications (incl.Internet); Neural networks; Ontology; Parameter estimation; Particle swarm optimization; Precipitation; Prediction models; River systems; Rivers; Root-mean-square errors; Sediment load; Sediments; Simulation and Modeling; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Stream discharge; Stream flow; Suspended load; Suspended sediments; Water quality; Water resources; Water resources management; Hyperparameter; SFLA-ANN; Meta-heuristic algorithms; Optimize; Prediction; Suspended sediment load
• ISSN: 1865-0473; 1865-0481
• DOI: 10.1007/s12145-024-01338-y

Suspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Yeşilırmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973–2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R 2 ), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R 2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R 2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R 2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures’ effective lifespan and operation and take precautions against sediment-related disaster risks.