Quantifying the Uncertainties in Data-Driven Models for Reservoir Inflow Prediction

Xiaoli Zhang (), Haixia Wang (), Anbang Peng, Wenchuan Wang, Baojian Li and Xudong Huang
Additional contact information
Xiaoli Zhang: North China University of Water Resources and Electric Power
Haixia Wang: Ludong University
Anbang Peng: Nanjing Hydraulic Research Institute
Wenchuan Wang: North China University of Water Resources and Electric Power
Baojian Li: North China University of Water Resources and Electric Power
Xudong Huang: North China University of Water Resources and Electric Power

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 4, No 12, 1479-1493

Abstract: Abstract Reservoir inflow prediction is subject to high uncertainties in data-driven modelling. In this study, a decomposition scheme is proposed to evaluate the individual and combined contributions of uncertainties from input sets and data-driven models to the total predictive uncertainty. Six variables (i.e., inflow (Q), precipitation (P), relative humidity (H), minimum temperature (Tmin), maximum temperature (Tmax) and precipitation forecast (F)), and three data-driven models (i.e., artificial neural network (ANN), support vector machine (SVM), and adaptive neuro fuzzy inference systems (ANFIS)) are used to produce an ensemble of 10-day inflow forecast for Huanren reservoir in China, and the analysis of variance (ANOVA) method is employed to decompose the uncertainty. The ensemble forecast results show that when the three variables, i.e., Q, P and F, are used only, the predictive accuracy of the data-driven models is very high and the addition of the other three variables, i. e., H, Tmin and Tmax, can slightly improve the predictive accuracy. The decomposition results indicate that the input set is the dominant source of uncertainty, the contribution of the data-driven model is limited and has a strong seasonal variation: larger in winter and summer, smaller in spring and autumn. Most importantly, the interactive contribution of the input set and the data-driven model to the total predictive uncertainty is very high and is more significant than the individual contribution from the model itself, implying that the combined effects of the input set and the data-driven model should be carefully considered in the modelling process.

Keywords: ANVOA; Data-driven model; Inflow prediction; uncertainty analysis; Sensitivity analysis (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11269-020-02514-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:34:y:2020:i:4:d:10.1007_s11269-020-02514-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-020-02514-7

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().