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

Content Provider	Springer Nature Link
Author	Kan, Guangyuan Yao, Cheng Li, Qiaoling Li, Zhijia Yu, Zhongbo Liu, Zhiyu Ding, Liuqian He, Xiaoyan Liang, Ke
Copyright Year	2015
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.
Starting Page	1345
Ending Page	1370
Page Count	26
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	29
Issue Number	5
e-ISSN	14363259
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-02-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Rainfall-runoff simulation Non-updating Partial mutual information Ensemble artificial neural network K-nearest neighbor algorithm PEK NU-PEK Math. Application in Environmental Science Earth Sciences Probability Theory and Stochastic Processes Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Computational Intelligence Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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