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Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model

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Abstract

An ensemble artificial neural network (ENN) based hybrid function approximator (named PEK), integrating the partial mutual information (PMI) based separate input variable selection (IVS) scheme, ENN-based output estimation, and K-nearest neighbor regression based output error estimation, has been proposed to improve event-based rainfall-runoff (RR) simulation. A hybrid data-driven RR model, named non-updating PEK (NU-PEK), is also developed on the basis of the PEK approximator. The rainfall and simulated antecedent discharges input variables for the NU-PEK model are selected separately by using a PMI-based IVS algorithm. A newly proposed candidate rainfall input set, sliding window cumulative rainfall is also proposed. These two methods are integrated to make a good compromise between the adequacy and parsimony of the input information and make contribution to the understandings of the hydrologic responses to the regional precipitation. The number of component networks and the topology and parameter settings of each component network are optimized simultaneously by using the multi-objective NSGA-II optimization algorithm and the early stopping Levenberg–Marquardt algorithm. The optimal combination weights of the ENN are obtained according to the Akaike information criterions of component networks. By combining all these methods, the simulation accuracy and generalization property of the PEK approximator are much better than traditional artificial neural network. The NU-PEK model is constructed by combining the PEK approximator with a newly proposed non-updating modeling approach to improve event-based RR simulation. The NU-PEK model was applied to three Chinese catchments for RR simulation and compared with two popular RR models, including the conceptual Xinanjiang model and the conceptual-data-driven IHACRES model. The results of simulation and sensitivity analysis indicate that the developed model generally outperforms the other two models. The NU-PEK model is capable of producing high accuracy non-updating RR simulation without the use of the real-time information, e.g. the observed discharges at previous time steps.

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Acknowledgments

This research was funded by the NNSF of China (Nos. 41130639, 51179045, 41101017, and 41201028), the Research and Innovation Program of Graduate of Colleges and Universities of Jiangsu Province China (No. CXZZ11_0435), the Specific Research of China Institute of Water Resources and Hydropower Research (No. Fangji 1240), the National Science and Technology Support Project (No. 2012BAK10B04), and the China Institute of Water Resource and Hydropower Research under the Project “Numerical model for flow simulation in 1D complex channel networks based on the Godunov method” (No. JZ0145B032014).

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Authors and Affiliations

  1. 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, Liuqian Ding & Xiaoyan He

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

    Cheng Yao, Qiaoling Li, Zhijia Li, Zhongbo Yu & Ke Liang

  3. Hydrology Bureau of Ministry of Water Resources, Beijing, 100053, People’s Republic of China

    Zhiyu Liu

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  1. Guangyuan Kan
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Correspondence to Cheng Yao.

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Kan, G., Yao, C., Li, Q. et al. Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model. Stoch Environ Res Risk Assess 29, 1345–1370 (2015). https://doi.org/10.1007/s00477-015-1040-6

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