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Environmental Earth Sciences

, Volume 74, Issue 6, pp 5039–5048 | Cite as

A threshold artificial neural network model for improving runoff prediction in a karst watershed

  • Xianmeng Meng
  • Maosheng Yin
  • Libo Ning
  • Dengfeng Liu
  • Xianwu Xue
Original Article

Abstract

Artificial neural network model (ANN) has been extensively used in hydrological prediction. Generally, most existing rainfall-runoff models including artificial neural network model are not very successful at simulating streamflow in karst watersheds. Due to the complex physical structure of karst aquifer systems, runoff generation processes are quite different during flood and non-flood periods. In this paper, an ANN model based on back-propagation algorithm was developed to simulate and predict daily streamflow in karst watersheds. The idea of threshold was introduced into artificial neural network model [hereafter called Threshold-ANN model (T-ANN)] to represent the nonlinear characteristics of the runoff generation processes in the flood and non-flood periods. The T-ANN model is applied to the Hamajing watershed, which is a small karst watershed in Hubei Province, China. The network input, the previous discharge, is determined by the correlative analysis, and the network structure is optimized with the maximum Nash coefficient as the objective function. And the precipitation and previous discharge are chosen as the threshold factors to reflect the effect of specificity of karst aquifer systems, respectively. By using the T-ANN, the simulation errors of streamflow have been reduced, and the simulation becomes more successful, which would be helpful for runoff prediction in karst watersheds.

Keywords

Artificial neural network Karst watershed Simulation Threshold 

Notes

Acknowledgments

This research was supported by the National Natural Science Foundation of China (51109192) and the Fundamental Research Funds for National University, China University of Geosciences (Wuhan) (CUGL100220) and Juzheng Fund on Environmental Protection (201203904).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xianmeng Meng
    • 1
  • Maosheng Yin
    • 1
  • Libo Ning
    • 1
  • Dengfeng Liu
    • 2
  • Xianwu Xue
    • 3
  1. 1.School of Environmental StudiesChina University of GeosciencesWuhanChina
  2. 2.School of Water Resources and HydropowerXi’an University of TechnologyXi’anChina
  3. 3.School of Civil Engineering and Environmental SciencesUniversity of Oklahoma, National Weather CenterNormanUSA

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