Water (Jan 2017)

Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting

  • Jianjin Wang,
  • Peng Shi,
  • Peng Jiang,
  • Jianwei Hu,
  • Simin Qu,
  • Xingyu Chen,
  • Yingbing Chen,
  • Yunqiu Dai,
  • Ziwei Xiao

DOI
https://doi.org/10.3390/w9010048
Journal volume & issue
Vol. 9, no. 1
p. 48

Abstract

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Flooding contributes to tremendous hazards every year; more accurate forecasting may significantly mitigate the damages and loss caused by flood disasters. Current hydrological models are either purely knowledge-based or data-driven. A combination of data-driven method (artificial neural networks in this paper) and knowledge-based method (traditional hydrological model) may booster simulation accuracy. In this study, we proposed a new back-propagation (BP) neural network algorithm and applied it in the semi-distributed Xinanjiang (XAJ) model. The improved hydrological model is capable of updating the flow forecasting error without losing the leading time. The proposed method was tested in a real case study for both single period corrections and real-time corrections. The results reveal that the proposed method could significantly increase the accuracy of flood forecasting and indicate that the global correction effect is superior to the second-order autoregressive correction method in real-time correction.

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