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A new hybrid data-driven model for event-based rainfall–runoff simulation

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Abstract

A new hybrid data-driven model named PBK has been proposed to improve the event-based rainfall–runoff simulation. The PBK is developed by coupling partial mutual information-based input variable selection (IVS), ensemble back-propagation neural network (EBPNN)-based discharge forecasting and K-nearest neighbor algorithm-based discharge error forecasting. This model is proposed for solving the hard problem of how to implement non-updating rainfall–runoff simulation by data-driven models. For the purpose of solving the hard problems, the PBK model has the following innovations and improvements: (1) a newly proposed non-updating modeling approach without the using of the real-time information and can obtain higher simulation accuracy; (2) a newly proposed IVS scheme and a newly proposed candidate rainfall input set to ensure the adequacy and parsimony of the rainfall and antecedent discharge input variables; and (3) a newly proposed calibration method for the EBPNN to ensure higher simulation accuracy and better generalization property. This method is a combination of the NGSA-II, Levenberg–Marquardt algorithm, and the AIC-based combination weights generating method. For the purpose of comparing simulation accuracy with traditional non-updating data-driven models, a back-propagation neural network model (PB_R) and a linear model (CLS) were also studied. This study utilized event flood data of Dongwan catchment for intercomparisons between different models. The simulation results indicated that the PBK model outperforms other data-driven models and has higher accuracy and better forecasting capability.

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Acknowledgments

This research was funded by the IWHR Scientific Research Projects of Outstanding Young Scientists “Research and application on the fast global optimization method for the Xinanjiang model parameters based on the high-performance heterogeneous computing”, the Study on Mass-Energy Balance Coupling Scheme on Hill-Slope and Its Application to Distributed Hydrological Models (No. 51420105014), the Third Sub-Project: Flood Forecasting, Controlling and Flood Prevention Aided Software Development—Flood Control Early Warning Communication System and Flood Forecasting, Controlling and Flood Prevention Aided Software Development for Poyang Lake Area of Jiangxi Province (0628-136006104242, JZ0205A432013, SLXMB200902), the Numerical Simulation Technology of Flash Flood based on Godunov Scheme and Its Mechanism Study by Experiment (51509263), the Mayor Seismic-Geological Disaster Chains Process and Disaster Risk Comprehensive Assessment—supported by National Sci-Tech Support Plan (2012bak10b03-02), the IWHR Special Dynamic Investigation Project on International Water Resources and Hydropower Technology Development—Progress Summary and Comment on the Research of Dynamic Control of Reservoir Water Level During Flood Season (JZ0145C102015), and the NNSF of China, Estimation of regional evapotranspiration using remotely sensed data based on the theoretical VFC/LST trapezoid space (No. 41501415). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

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Authors and Affiliations

  1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, 100038, People’s Republic of China

    Guangyuan Kan, Jiren Li, Liuqian Ding, Xiaoyan He, Xiaoming Jiang, Minglei Ren, Hui Li, Fan Wang & Zhongbo Zhang

  2. Cooperative Innovation Center for Water Safety and Hydro-Science of Hohai University, Nanjing, 210098, People’s Republic of China

    Xingnan Zhang

  3. College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, People’s Republic of China

    Ke Liang

  4. Hydrologic Bureau (Information Center) of the Huaihe River Commission, Bengbu, 233001, People’s Republic of China

    Youbing Hu

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  1. Guangyuan Kan
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Correspondence to Guangyuan Kan.

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Kan, G., Li, J., Zhang, X. et al. A new hybrid data-driven model for event-based rainfall–runoff simulation. Neural Comput & Applic 28, 2519–2534 (2017). https://doi.org/10.1007/s00521-016-2200-4

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