Satellite-based estimation of daily suspended sediment load using hybrid intelligent models

This study uses a combination of support vector regression models, particle swarm optimization, and grey wolf optimization algorithms to predict suspended sediment load. For this purpose, The Satellite Precipitation of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Global Land Data Assimilation System (GLDAS) soil moisture products are utilized as the predictors. The prediction models are evaluated based on various visual and quantitative indicators. The Taylor and radar diagrams confirm that the support vector regression-particle swarm optimization best agrees with the observed values. Moreover, the obtained quantitative indices show that the support vector regression-particle swarm optimization model offers better performance than other models used in the present study. The values of the best indices are: Pearson correlation coefficient of 0.997, relative root mean square error of 13.17, percentage bias of 4.05, and Nash-Sutcliffe efficiency of 0.995.