Saeed SamadianfardKatayoun KargarSadra ShadkaniSajjad HashemiAkram AbbaspourMir Jafar Sadegh Safari2025-10-06202214333058, 094106430941-06431433-305810.1007/s00521-021-06550-1https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116584155&doi=10.1007%2Fs00521-021-06550-1&partnerID=40&md5=fd2d46e752b795e71589427251ca461ehttps://gcris.yasar.edu.tr/handle/123456789/8750Owing to the nonlinear and non-stationary nature of the suspended sediment transport in rivers suspended sediment concentration (SSC) modeling is a challenging task in environmental engineering. Investigation of SSC is of paramount importance in river morphology and hydraulic structures operation. To this end for SSC modeling first random forest (RF) and multi-layer perceptron (MLP) standalone models were developed and then they were optimized with genetic algorithm (GA) and stochastic gradient descent (SGD) to develop GA-MLP GA-RF SGD-MLP and SGD-RF hybrid models. Variety of input scenarios are implemented for SSC prediction to find the best input combination. The streamflow and SSC data collected from two stations of Minnesota and San Joaquin rivers respectively located at South Dakota and California are utilized in the current study. Accuracies of the developed models are examined by means of three performance criteria of correlation coefficient (CC) scattered index (SI) and Willmott’s index of agreement (WI). A significant promotion in accuracy of hybrid models has been seen in contrast to their standalone counterparts. As can be deduced from the results GA-MLP-5 and GA-RF-5 models with CC of 0.950 and 0.944 SI of 0.290 and 0.308 and WI of 0.974 and 0.971 respectively were found as best models for prediction of SSC at Minnesota river. The developed SGD-MLP-5 and SGD-RF-5 models with CC of 0.900 and 0.901 SI of 0.339 and 0.339 and WI of 0.945 and 0.946 respectively gave accurate results at San Joaquin river. Through the application of SGD algorithm the adaptive learning rate epochs rho L1 and L2 were activated and presumed as 0.004 10 1 0.000009 and 0 respectively. The ExpRectifier was considered as san activation operation due to its better efficiency in comparison with its alternatives for predicting SSC in SGD-MLP model. According to the results the fifth scenario that incorporates SSC<inf>t–1</inf> SSC<inf>t–2</inf> Q<inf>t</inf> Q<inf>t–1</inf> and Q<inf>t–2</inf> were found superior for SSC modeling in the studied rivers. The recommended hybrid algorithms based on GA and SGD optimization algorithms are proposed as practical tools for solving complex environmental problems. © 2022 Elsevier B.V. All rights reserved.EnglishGenetic Algorithm, Hybrid Model, Prediction, Random Forest, River, Stochastic Gradient Descent, Suspended Sediment, Decision Trees, Forecasting, Genetic Algorithms, Gradient Methods, Rivers, Sediment Transport, Sedimentation, Stochastic Models, Stochastic Systems, Concentration Model, Correlation Coefficient, Hybrid Model, Multilayers Perceptrons, Nonstationary, Random Forests, San Joaquin River, Stochastic Gradient Descent, Stochastic Gradient Descent Method, Suspended Sediments Concentration, Suspended SedimentsDecision trees, Forecasting, Genetic algorithms, Gradient methods, Rivers, Sediment transport, Sedimentation, Stochastic models, Stochastic systems, Concentration model, Correlation coefficient, Hybrid model, Multilayers perceptrons, Nonstationary, Random forests, San Joaquin River, Stochastic gradient descent, Stochastic gradient descent method, Suspended sediments concentration, Suspended sedimentsArticle