Abstract
Statistical methods have been widely used to build different streamflow prediction models; however, lacking of physical mechanism prevents precise streamflow prediction in alpine regions dominated by rainfall, snow and glacier. To improve precision, a new hybrid model (HBNN) integrating HBV hydrological model, Bayesian neural network (BNN) and uncertainty analysis is proposed. In this approach, the HBV is mainly used to generate initial snow-melt and glacier-melt runoffs that are regarded as new inputs of BNN for precision improvement. To examine model reliability, a hybrid deterministic model called HLSSVM incorporating the HBV model and least-square support vector machine is also developed and compared with HBNN in a typical region, the Yarkant River basin in Central Asia. The findings suggest that the HBNN model is a robust streamflow prediction model for alpine regions and capable of combining strengths of both the BNN statistical model and the HBV hydrological model, providing not only more precise streamflow prediction but also more reasonable uncertainty intervals than competitors particularly at high flows. It can be used in predicting streamflow for similar regions worldwide.
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Acknowledgements
The work was jointly supported by International Co-Foundation NSFC-ANR (NSFC-41561134016, ANR-15-CE01-20 0011), the grants from the National Natural Science Foundation of China (41561134016), a key grant of Chinese Academy of Sciences (KZZD-EW-12) and a grant from the Ministry of Education (2015B25714). The authors are grateful to the editors and the anonymous reviewers for their insightful comments and suggestions.
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Ren, W.W., Yang, T., Huang, C.S. et al. Improving monthly streamflow prediction in alpine regions: integrating HBV model with Bayesian neural network. Stoch Environ Res Risk Assess 32, 3381–3396 (2018). https://doi.org/10.1007/s00477-018-1553-x
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DOI: https://doi.org/10.1007/s00477-018-1553-x