Abstract

Predicting suspended sediment load in water resource management requires efficient and reliable predicted models. This study considers the support vector regression method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization method is integrated with the SVR model to provide a novel hybrid predictive model. The SVR combined with the genetic algorithm is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of rainfall and discharge data of Cham Siah River catchment are provided. The predictive models are assessed using various numerical and visual indicators. The results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBO model offers highest Pearson correlation coefficient, Willmott’s Index, ratio of performance to IQ, and modified index of agreement and the lowest relative root mean square error in comparison with SVR-GA and SVR models, respectively.