A comparative study of three different learning algorithms applied to ANFIS for predicting daily suspended sediment concentration

作     者：Keivan Kaveh Minh Duc Bui Peter Rutschmann 

作者机构：Department of hydraulic and water resource engineering/School of civil engineering Technische Universitt Mnchen (TUM) 

出 版 物：《International Journal of Sediment Research》 (国际泥沙研究(英文版))

年 卷 期：2017年第32卷第3期

页      面：340-350页

核心收录：

学科分类：0709[理学-地质学] 0819[工学-矿业工程] 08[工学] 0818[工学-地质资源与地质工程] 0708[理学-地球物理学] 0903[农学-农业资源与环境] 081502[工学-水力学及河流动力学] 0815[工学-水利工程] 0816[工学-测绘科学与技术] 

主　　题：Fuzzy Inference System Hybrid learning rule Levenberg-Marquardt algorithm Schuylkill river Suspended sediments 

摘      要：The modeling and prediction of suspended sediment in a river are key elements in global water recourses and environment policy and management. In the present study, an Adaptive Neuro-Fuzzy Inference System model trained with the Levenberg-Marquardt learning algorithm is considered for time series modeling of suspended sediment concentration in a river. The model is trained and validated using daily river discharge and suspended sediment concentration data from the Schuylkill River in the United States. The results of the proposed method are evaluated and compared with similar networks trained with the common Hybrid and Back-Propagation algorithms, which are widely used in the literature for prediction of suspended sediment concentration. Obtained results demonstrate that models trained with the Hybrid and Levenberg-Marquardt algorithms are comparable in terms of prediction ***, the networks trained with the Levenberg-Marquardt algorithm perform better than those trained with the Hybrid approach.