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Uncertainty assessment and optimization of hydrological model with the Shuffled Complex Evolution Metropolis algorithm: an application to artificial neural network rainfall-runoff model

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Abstract

Assessment of parameter and predictive uncertainty of hydrologic models is an essential part in the field of hydrology. However, during the past decades, research related to hydrologic model uncertainty is mostly done with conceptual models. As is accepted that uncertainty in model predictions arises from measurement errors associated with the system input and output, from model structural errors and from problems with parameter estimation. Unfortunately, non-conceptual models, such as black-box models, also suffer from these problems. In this paper, we take the artificial neural network (ANN) rainfall-runoff model as an example, and the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA) is employed to analysis the parameter and predictive uncertainty of this model. Furthermore, based on the results of uncertainty assessment, we finally arrive at a simpler incomplete-connection artificial neural network (ICANN) model as well as with better performance compared to original ANN rainfall-runoff model. These results not only indicate that SCEM-UA can be a useful tool for uncertainty analysis of ANN model, but also prove that uncertainty does exist in ANN rainfall-runoff model. Additionally, in some way, it presents that the ICANN model is with smaller uncertainty than the original ANN model.

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Acknowledgments

The authors thank Professor Jasper A. Vrugt for recommending this topic for research as well as providing the source code of the SCEM-UA algorithm and the data used in the application. Also, special thanks are given to the anonymous reviewers and editors for their constructive comments. This work is supported by the Special Research Foundation for the Public Welfare Industry of the Ministry of Water Resources (Project No. 201001080) and the research funds of University and college PhD discipline of China (No. 20100142110012).

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  1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jun Guo, Jianzhong Zhou, Qiang Zou & Xiaofan Zeng

  2. Laboratory of Numerical Modeling Technique for Water Resources, Department of Water Resources and Environment, Pearl River Water Resources Research Institute, Guangzhou, 510623, China

    Lixiang Song

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  1. Jun Guo
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Correspondence to Jianzhong Zhou.

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Guo, J., Zhou, J., Song, L. et al. Uncertainty assessment and optimization of hydrological model with the Shuffled Complex Evolution Metropolis algorithm: an application to artificial neural network rainfall-runoff model. Stoch Environ Res Risk Assess 27, 985–1004 (2013). https://doi.org/10.1007/s00477-012-0639-0

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