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Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting

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  • Hairong Zhang
  • Jianzhong Zhou
  • Lei Ye
  • Xiaofan Zeng
  • Yufan Chen

Abstract

It is widely accepted that Prediction Interval (PI) can provide more accurate and precise information than deterministic forecast when the uncertainty level increases in flood forecasting. Coverage probability and PI width are two main criteria used to assess the constructed PI, rarely has there been an index to quantify the symmetry between target value and PI. This study extends a newly proposed PI estimation method called Lower Upper Bound Estimation (LUBE) method, which adopts an Artificial Neural Network (ANN) with two outputs to directly generate the upper and lower bounds of PI without making any assumption about the data distribution. A new Prediction Interval Symmetry (PIS) index is introduced and a new objective function is developed for the comprehensive evaluation of PI considering their coverage probability, width and symmetry. Furthermore, Shuffled Complex Evolution algorithm (SCE-UA) is used to minimize the objective function and optimize ANN parameters in the LUBE method. The proposed method is applied to a real world flood forecasting case study of the upper Yangtze River Watershed. The result shows that the SCE-UA based LUBE method with new objective function is very efficient, meanwhile, the midpoint forecasting of the PI obtains excellent performance by evidently improving the symmetry of PI. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Hairong Zhang & Jianzhong Zhou & Lei Ye & Xiaofan Zeng & Yufan Chen, 2015. "Lower Upper Bound Estimation Method Considering Symmetry for Construction of Prediction Intervals in Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5505-5519, December.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:15:p:5505-5519
    DOI: 10.1007/s11269-015-1131-7
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    2. Tian Peng & Chu Zhang & Jianzhong Zhou & Xin Xia & Xiaoming Xue, 2019. "Multi-Objective Optimization for Flood Interval Prediction Based on Orthogonal Chaotic NSGA-II and Kernel Extreme Learning Machine," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4731-4748, November.
    3. Vahid Nourani & Nardin Jabbarian Paknezhad & Hitoshi Tanaka, 2021. "Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    4. Jianzhong Zhou & Kuaile Feng & Yi Liu & Chao Zhou & Feifei He & Guangbiao Liu & Zhongzheng He, 2020. "A Hydrologic Uncertainty Processor Using Linear Derivation in the Normal Quantile Transform Space," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3649-3665, September.
    5. Wei Li & Jianzhong Zhou & Lu Chen & Kuaile Feng & Hairong Zhang & Changqing Meng & Na Sun, 2019. "Upper and Lower Bound Interval Forecasting Methodology Based on Ideal Boundary and Multiple Linear Regression Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(3), pages 1203-1215, February.
    6. Wei Li & Jianzhong Zhou & Huaiwei Sun & Kuaile Feng & Hairong Zhang & Muhammad Tayyab, 2017. "Impact of Distribution Type in Bayes Probability Flood Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(3), pages 961-977, February.

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