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Water 2017, 9(1), 48; doi:10.3390/w9010048

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

1
College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
2
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
3
Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV 89119, USA
4
Bureau of Hydrology, MWR, Beijing 100053, China
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Franchini
Received: 2 November 2016 / Revised: 21 December 2016 / Accepted: 5 January 2017 / Published: 13 January 2017
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Abstract

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. View Full-Text
Keywords: flood forecasting; real-time correction; BP neural networks; XAJ model flood forecasting; real-time correction; BP neural networks; XAJ model
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MDPI and ACS Style

Wang, J.; Shi, P.; Jiang, P.; Hu, J.; Qu, S.; Chen, X.; Chen, Y.; Dai, Y.; Xiao, Z. Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting. Water 2017, 9, 48.

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