A Hybrid Double Feedforward Neural Network for Suspended Sediment Load Estimation

Xiao Yun Chen, Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

107 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2179-2194
Number of pages16
JournalWater Resources Management
Volume30
Issue number7
DOIs
Publication statusPublished - 1 May 2016

Keywords

  • Differential evolution
  • Double parallel feedforward
  • Hybrid neural network
  • River flow
  • Suspended sediment load

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Water Science and Technology

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