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Inicio  /  Water  /  Vol: 9 Par: 1 (2017)  /  Artículo
ARTÍCULO
TITULO

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 and Ziwei Xiao    

Resumen

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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