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Water 2017, 9(1), 48;

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

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, NV 89119, USA
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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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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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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