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

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...