@article{ed9cd4b96fdd4e02a91767c9d2947589,
title = "Efficient estimation of flood forecast prediction intervals via single- and multi-objective versions of the LUBE method",
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.",
keywords = "LUBE, artificial neural networks, flood forecasting, multi-objective, prediction interval, uncertainty",
author = "Lei Ye and Jianzhong Zhou and Gupta, {Hoshin V.} and Hairong Zhang and Xiaofan Zeng and Lu Chen",
note = "Funding Information: This work was supported by the Key Program of the Major Research Plan of the National Natural Science Foundation of China (No. 91547208), the State Key Program of National Natural Science of China (No. 51239004) and the National Natural Science Foundation of China (No. 51579107, 51309105, 51309104). The third author received partial support from the Australian Research Council through the Centre of Excellence for Climate System Science (No. CE110001028) and from the EU-funded project {\textquoteleft}Sustainable Water Action: Building Research Links Between EU and US{\textquoteright} (INCO-20011-7.6 No. 294947). We especially thank Mr Hao Quan (National University of Singapore) and Dr Dongwei Gui (Cele National Station of Observation and Research for Desert-Grassland Ecosystem in Xinjiang) for their valuable suggestions and support. Publisher Copyright: {\textcopyright} 2015 John Wiley & Sons, Ltd.",
year = "2016",
month = jul,
day = "15",
doi = "10.1002/hyp.10799",
language = "English (US)",
volume = "30",
pages = "2703--2716",
journal = "Hydrological Processes",
issn = "0885-6087",
publisher = "John Wiley and Sons Ltd",
number = "15",
}