Developing sediment concentration prediction in the Euphrates River catchment, Türkiye, with a honey badger and coati optimization-based hybrid algorithm

Abstract

Estimation of sediment concentration (SC) is of vital importance in terms of siltation and economic life of dams, lakes and aqueducts, reservoir operations, design of water resource structures, monitoring and control of water pollution, and flood management. Direct measurement of SC is a challenging and expensive task. For these reasons, it was used to estimate the SC values at a station in the Euphrates River. New hybrid models were established by combining the CatBoost regressor (CBR) and artificial neural network (ANN) models with the honey badger optimization algorithm (HBA) and coati optimization algorithm (COA). The performance of the new model was compared with stand-alone model of ANN and CBR, and their accuracy was evaluated. In the setup of the models, 4 different input combinations of lagged sediment and discharge values for up to 3 months were evaluated. It is noteworthy that as the number of input variables, i.e., lagged data input, increases, the prediction accuracy of the models generally increases. HBA and COA algorithms often improve the accuracy of sediment prediction by optimizing the parameters of the single machine learning model. In addition, according to the AIC performance metric, the HBA algorithm is generally slightly better capable of optimization than the COA. The best model outputs were obtained according to the HBA-CBR hybrid approach of scenario 4 (RMSE = 59.78, AIC = 785.03, R2 = 0.32, PBIAS = 0.016, SI = 0.48, and MBE = - 2.05 in test phase), which consists of discharge and sediment with a delay of up to 3 months. The results of the study are valuable for decision-makers and planners in terms of practical reservoir and flood management and protection of coasts and river beds.