Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
AbstractFlooding 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
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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.
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(1):48.Chicago/Turabian Style
Wang, Jianjin; Shi, Peng; Jiang, Peng; Hu, Jianwei; Qu, Simin; Chen, Xingyu; Chen, Yingbing; Dai, Yunqiu; Xiao, Ziwei. 2017. "Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting." Water 9, no. 1: 48.
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