Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
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...
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| Published in | Water (Basel) Vol. 9; no. 1; p. 48 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
01.01.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2073-4441 2073-4441 |
| DOI | 10.3390/w9010048 |
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| Abstract | 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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| AbstractList | 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. |
| Audience | Academic |
| Author | Shi, Peng Chen, Xingyu Qu, Simin Jiang, Peng Dai, Yunqiu Wang, Jianjin Xiao, Ziwei Hu, Jianwei Chen, Yingbing |
| Author_xml | – sequence: 1 givenname: Jianjin surname: Wang fullname: Wang, Jianjin – sequence: 2 givenname: Peng surname: Shi fullname: Shi, Peng – sequence: 3 givenname: Peng surname: Jiang fullname: Jiang, Peng – sequence: 4 givenname: Jianwei surname: Hu fullname: Hu, Jianwei – sequence: 5 givenname: Simin orcidid: 0000-0002-1450-1194 surname: Qu fullname: Qu, Simin – sequence: 6 givenname: Xingyu surname: Chen fullname: Chen, Xingyu – sequence: 7 givenname: Yingbing surname: Chen fullname: Chen, Yingbing – sequence: 8 givenname: Yunqiu surname: Dai fullname: Dai, Yunqiu – sequence: 9 givenname: Ziwei surname: Xiao fullname: Xiao, Ziwei |
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| StartPage | 48 |
| SubjectTerms | 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 |
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| Title | Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting |
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