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

• Main Authors: Jianjin Wang, Peng Shi, Peng Jiang, Jianwei Hu, Simin Qu, Xingyu Chen, Yingbing Chen, Yunqiu Dai, Ziwei Xiao
• Format: Article
• Language: English
• Published: MDPI AG 2017-01-01
• Series: Water
• Subjects: flood forecasting; real-time correction; BP neural networks; XAJ model
• Online Access: http://www.mdpi.com/2073-4441/9/1/48

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.