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

• Published in: Water (Basel) Vol. 9; no. 1; p. 48
• Main Authors: Wang, Jianjin, Shi, Peng, Jiang, Peng, Hu, Jianwei, Qu, Simin, Chen, Xingyu, Chen, Yingbing, Dai, Yunqiu, Xiao, Ziwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2017
• Subjects: Accuracy; Algorithms; Back propagation; Case studies; China; disasters; Flood forecasting; Floods; Forecasting; Freshwater; hydrologic models; Hydrology; Knowledge; Mean square errors; Neural networks; Precipitation; Propagation; Runoff; Stream flow; Teaching methods; Watersheds; China; China, People's Republic
• ISSN: 2073-4441; 2073-4441
• DOI: 10.3390/w9010048

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.