Assessment of novel nature-inspired fuzzy models for predicting long contraction scouring and related uncertainties

作     者：Ahmad SHARAFATI Masoud HAGHBIN Mohammadamin TORABI Zaher Mundher YASEEN Ahmad SHARAFATI;Masoud HAGHBIN;Mohammadamin TORABI;Zaher Mundher YASEEN

作者机构：Department of Civil EngineeringScience and Research BranchIslamic Azad UniversityTehranIran Department of Structural Mechanics&Hydraulics EngineeringUniversity of GranadaGranada18001Spain EIT.Schnabel EngineeringChadds FordPA19317USA New Era and Development in Civil Engineering Research GroupScientific Research CenterAl-Ayen UniversityThi-Qar64001Iraq 

出 版 物：《Frontiers of Structural and Civil Engineering》 (结构与土木工程前沿（英文版）)

年 卷 期：2021年第15卷第3期

页      面：665-681页

核心收录：

学科分类：08[工学] 080104[工学-工程力学] 0815[工学-水利工程] 0801[工学-力学（可授工学、理学学位）] 

主　　题：long contraction scour prediction uncertainty ANFIS model meta-heuristic algorithm 

摘      要：The scouring phenomenon is one of the major problems experienced in hydraulic *** this study,an adaptive neuro-fuzzy inference system is hybridized with several evolutionary approaches,including the ant colony optimization,genetic algorithm,teaching-learning-based optimization,biogeographical-based optimization,and invasive weed optimization for estimating the long contraction scour *** proposed hybrid models are built using non-dimensional information collected from previous *** proposed hybrid intelligent models are evaluated using several statistical performance metrics and graphical ***,the uncertainty of models,variables,and data are *** on the achieved modeling results,adaptive neuro-fuzzy inference system-biogeographic based optimization(ANFIS-BBO)provides superior prediction accuracy compared to others,with a maximum correlation coefficient(R_(test)=0.923)and minimum root mean square error value(RMSE_(test)=0.0193).Thus,the proposed ANFIS-BBO is a capable cost-effective method for predicting long contraction scouring,thus,contributing to the base knowledge of hydraulic structure sustainability.