Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method |
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| Authors: | Lei Ye Jianzhong Zhou Hoshin V Gupta Hairong Zhang Xiaofan Zeng Lu Chen |
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| Institution: | 1. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, China;2. Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan, China;3. Department of Hydrology and Water Resources, The University of Arizona, Tucson, AZ, USA |
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| Abstract: | Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd. |
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| Keywords: | prediction interval uncertainty LUBE flood forecasting multi‐objective artificial neural networks |
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