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.
Highlights
Each year, significant social and economic losses and casualties are caused by extreme storms around the world, especially in the regions dominated by monsoon climate and areas with slow development of water conservancy projects [1,2,3,4,5]
We proposed to combine artificial neural networks (ANNs) with the traditional hydrological model
The results indicated that significant improvement had been achieved in respects of mean Nash–Sutcliffe efficiency coefficient (NSE), δR and δQ after correction by amount of datasets required as the flood events selected to conduct single period correction can be reused in this test
Summary
Significant social and economic losses and casualties are caused by extreme storms around the world, especially in the regions dominated by monsoon climate and areas with slow development of water conservancy projects [1,2,3,4,5]. Flood forecasting is one of the most important non-structural measures for flood control [6,7]. The accuracy of forecasting would directly impact on the reservoir operation, flood control and rescue measures [8]. One of the challenges in flood forecasting is model selection [9]. Current hydrologic forecasting is mainly divided into two categories, namely knowledge-based methods and data-driven methods [10]. Knowledge-based methods including both conceptual and physical approaches have been widely accepted and applied because they have definite hydrologic meaning [11,12,13,14]. Hydrological models tend to have large number of parameters that need to be calibrated and the optimal parameters can hardly be obtained [15]. The developed digital information technology is capable of handling massive data and extracting and reusing information implicitly existing in the hydrologic
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