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

作　　者：Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi 

机构地区：[1]State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing,210098,China [2]Department of Urban Planning,Engineering Networks and Systems,Institute of Architecture and Construction,South Ural State University,76,Lenin Prospect,454080 Chelyabinsk,Russia [3]New Era and Development in Civil Engineering Research Group,Scientific Research Center,Al-Ayen University,Thi-Qar,64001,Iraq [4]Faculty of Science,Agronomy Department,Hydraulics Division University,20 Août 1955,Route El Hadaik,BP 26,Skikda,Algeria [5]School of Civil Engineering,Faculty of Engineering,Universiti Teknologi Malaysia(UTM),Johor Bahru,81310,Malaysia [6]Geoinformation Tech.Center of Excellence,Faculty of Geomatics Engineering,K.N.Toosi University of Technology,Tehran,Iran [7]Department of Computer Science and Engineering,Sejong University,Seoul,Republic of Korea [8]Civil Engineering Department,Ilia State University,Tbilisi,Georgia,USA

出　　处：《International Journal of Sediment Research》2022年第3期383-398,共16页国际泥沙研究（英文版）

摘　　要：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 eSutcliffe 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 

分 类 号：TV149[水利工程—水力学及河流动力学]