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A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation

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Abstract

Estimation of suspended sediment loads (SSL) in rivers is an important issue in water resources management and planning. This study proposes a hybrid double feedforward neural network (HDFNN) model for daily SSL estimation, by combining fuzzy pattern-recognition and continuity equation into a structure of double neural networks. A comparison is performed between HDFNN, multi-layer feedforward neural network (MFNN), double parallel feedforward neural network (DPFNN) and hybrid feedforward neural network (HFNN) models. Based on a case study on the Muddy Creek in Montana of USA, it is found that the HDFNN model is strongly superior to the other three benchmarking models in terms of root mean squared error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSEC). HDFNN model demonstrates the best generalization and estimation ability due to its configuration and capability of physically dealing with different inputs. The peak value of SSL is closely estimated by the HDFNN model as well. The performances of HDFNN model in low and medium loads are satisfactory when investigated by partitioning analysis. Thus, the HDFNN is appropriate for modeling the sediment transport process with nonlinear, fuzzy and time-varying characteristics. It explores a practical alternative for use and can be recommended as an efficient estimation model for SSL.

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Acknowledgments

This research was supported by Central Research Grant of Hong Kong Polytechnic University (4-ZZAD).

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, People’s Republic of China

    Xiao Yun Chen & Kwok Wing Chau

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  1. Xiao Yun Chen
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  2. Kwok Wing Chau
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Correspondence to Kwok Wing Chau.

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Chen, X.Y., Chau, K.W. A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation. Water Resour Manage 30, 2179–2194 (2016). https://doi.org/10.1007/s11269-016-1281-2

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  • DOI: https://doi.org/10.1007/s11269-016-1281-2

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