Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
In: Water; Volume 9; Issue 1; Pages: 48, 2017
academicJournal
Zugriff:
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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Application of BP Neural Network Algorithm in Traditional Hydrological Model for Flood Forecasting
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Autor/in / Beteiligte Person: | Wang, Jianjin ; Shi, Peng ; Jiang, Peng ; Hu, Jianwei ; Qu, Simin ; Chen, Xingyu ; Chen, Yingbing ; Dai, Yunqiu ; Xiao, Ziwei |
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Zeitschrift: | Water; Volume 9; Issue 1; Pages: 48, 2017 |
Veröffentlichung: | Multidisciplinary Digital Publishing Institute, 2017 |
Medientyp: | academicJournal |
DOI: | 10.3390/w9010048 |
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