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

• Published in: Hydrological Sciences Journal Vol. 69; no. 11; pp. 1501 - 1522
• Main Authors: Yu, Haijiao, Yang, Linshan, Feng, Qi, Barzegar, Rahim, Adamowski, Jan F., Wen, Xiaohu
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 17.08.2024; Informa UK Limited; Taylor & Francis Ltd
• Subjects: Algorithms; Arid regions; Arid zones; Bayesian analysis; Bayesian model averaging; Bayesian theory; Belief networks; China; Daily; Daily forecasts; Decomposition; Deep learning; Ensemble learning; Forecasting; Learning algorithms; Machine learning; Mathematical models; oases; oasis; Probability theory; Stream discharge; Stream flow; Streamflow forecasting; Supervised learning; Uncertainty analysis; China
• ISSN: 0262-6667; 2150-3435; 2150-3435
• DOI: 10.1080/02626667.2024.2374868

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.