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

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University 4. Faculty of Science Agronomy Department Hydraulics Division University Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM)

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 rating 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 performance was assessed using the root mean square error(RMSE), mean absolute error(MAE), Nash-Sutcliffe Efficiency(NSE), and coefficient of determination(R~2) 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 R~2, 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.

关键词： Suspended sediment load adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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