Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China

ABSTRACT

Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine learning and deep learning models. Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe efficiency values of 0.976, 0.967, and 0.957, BMA achieved the greatest accuracy for 1-, 2-, and 3-day-ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of BMA in yielding consistently accurate streamflow forecasts. Thus, BMA could provide an efficient alternative approach to multistep-ahead daily streamflow forecasting. The incorporation of data decomposition techniques (e.g. variational mode decomposition) and deep learning algorithms (e.g. deep belief network) into BMA may provide worthy technical references for supervised learning of streamflow systems in data-scarce regions.

KEYWORDS:

• Bayesian model averaging
• ensemble learning
• decomposition
• oasis
• streamflow forecasting

Editor A. Fiori; Associate Editor R. van Nooijen

Editor A. Fiori; Associate Editor R. van Nooijen

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Conceptualization, LY, HY, QF, and XW; methodology, LY, HY, and XW; software, HY and XW; validation, LY, RB, and JFA; investigation, LY, HY, QF, RB, JFA, and XW; writing – original draft preparation, LY, HY, and XW; writing – review and editing, QF, RB, and JFA; supervision, QF, HY, and XW. All authors read and agreed to the published version of the manuscript.

Data availability statement

The data used in this research are available from the corresponding author upon reasonable request.

Ethics approval

The authors confirm that this article is original research and has not been published or presented previously in any journal or conference in any language (in whole or in part).

Additional information

Funding

This work was supported by the Strategic Priority Research Program of CAS [Grant No. XDB0720202], the National Natural Science Fund of China [Grant No. 52379030, 42301361], the Young Elite Scientist Sponsorship Program of CAST [Grant No. YESS20200089], and the Youth Innovation Promotion Association of CAS [Grant No. 2022435].