Using HBMO an artificial intelligence model forestimating the total sediment load in three different riversin USA

Sediment discharge estimation is a crucial process for water resource management.Traditionally, sediment discharge is calculated based on direct measurements ofsediment concentration or through empirical equations for sediment transport. However,the results obtained from various sediment transport formulas often divergesignificantly from each other and from actual measured values. In this study, we employan artificial intelligence model called HBMO (Honey Bee Mating Optimization) and itsmodified version, MHBMO, to estimate sediment discharge in rivers. The keyparameters considered in this model are average flow velocity, water surface slope,average flow depth, median particle diameter, water temperature, and river width. Byutilizing this approach, we create a highly nonlinear mathematical model to estimate thetotal sediment load in rivers. The accuracy of our introduced model is fine-tuned usingthree stochastic parameters across different river systems

Keywords:

Artificial intelligence , HBMO , Sediment , Susitna River , Snake River , Chulitna River