Abstract
The application of a data-driven adaptive neuro-fuzzy modelling technique for predicting bed load and total bed-material load for the River Rhine is summarized. Four main parameters affecting sediment transport are used to construct the model, using 560 and 510 measured bed load and total bed-material load data, respectively. Two-thirds of the available data sets are used for training and one third for testing. The initial fuzzy model is obtained by grid partitioning of the input variables. The optimization of the model is performed by data-driven tuning of the fuzzy model parameters using the adaptive neuro-fuzzy inference system, so that the model output is able to reproduce the measured value. A sensitivity analysis for the combination of input parameters, as well as the number and type of membership functions, is also performed. The model results show that the data-driven adaptive neuro-fuzzy modelling approach can be a powerful alternative technique for estimating both bed load and total bed-material load. Editor D. Koutsoyiannis Citation Wieprecht, S., Tolossa, H.G., and Yang, C.T., 2013. A neuro-fuzzy-based modelling approach for sediment transport computation. Hydrological Sciences Journal, 58 (3), 587–599.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
