Predictability performance enhancement for suspended sediment in rivers:Inspection of newly developed hybrid adaptive neuro-fuzzy system model

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams,the durability of hydroelectric-equipment,river susceptibility to pollution,suitability for navigation,and potential for aesthetics and fish habitat.The capability of a new machine learning model,fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm (ANFIS-FCM-PSOGSA) in improving the estimation accuracy of river suspended sediment loads (SSLs) is investigated in the current study.The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization (ANFIS-FCM-PSO),ANFIS-FCM,and sediment rat-ing curve (SRC) models.Various input combinations involving lagged river flow (Q) and suspended sediment (S) values were used for model development.The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs.The model perfor-mance was assessed using the root mean square error (RMSE),mean absolute error (MAE),Nash-Sutcliffe Efficiency (NSE),and coefficient of determination (R2) and several graphical comparison methods.The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO (or ANFIS-FCM) models by 8.14％ (1.72％),14.7％ (5.71％),12.5％ (2.27％),and 25.6％ (1.86％),in terms of the RMSE,MAE,NSE and R2,respectively.The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load.The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.