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

被引：96

作者：

Wang, Jianjin ^{[1
]}

Shi, Peng ^{[1
,2
]}

Jiang, Peng ^{[2
,3
]}

Hu, Jianwei ^{[4
]}

Qu, Simin ^{[1
]}

Chen, Xingyu ^{[1
]}

Chen, Yingbing ^{[1
]}

Dai, Yunqiu ^{[1
]}

Xiao, Ziwei ^{[1
]}

机构：

[1] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Jiangsu, Peoples R China

[2] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul, Nanjing 210098, Jiangsu, Peoples R China

[3] Desert Res Inst, Div Hydrol Sci, Las Vegas, NV 89119 USA

[4] MWR, Bur Hydrol, Beijing 100053, Peoples R China

来源：

WATER | 2017年 / 9卷 / 01期

基金：

中国国家自然科学基金;

关键词：

flood forecasting; real-time correction; BP neural networks; XAJ model; XINANJIANG MODEL; ENSEMBLE; PREDICTION; RIVER;

D O I：

10.3390/w9010048

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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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页数：16

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