Efficient estimation of flood forecast prediction intervals via single- and multi-objective versions of the LUBE method

Lei Ye, Jianzhong Zhou, Hoshin Vijai Gupta, Hairong Zhang, Xiaofan Zeng, Lu Chen

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

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.

Original languageEnglish (US)
JournalHydrological Processes
DOIs
StateAccepted/In press - 2016

Keywords

  • Artificial neural networks
  • Flood forecasting
  • LUBE
  • Multi-objective
  • Prediction interval
  • Uncertainty

ASJC Scopus subject areas

  • Water Science and Technology

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