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Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method
Author(s) -
Ye Lei,
Zhou Jianzhong,
Gupta Hoshin V.,
Zhang Hairong,
Zeng Xiaofan,
Chen Lu
Publication year - 2016
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.10799
Subject(s) - flood myth , yangtze river , estimation , construct (python library) , computer science , watershed , flood forecasting , data mining , statistics , mathematics , machine learning , geography , management , archaeology , china , economics , programming language
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.