Bias learning improves data driven models for streamflow prediction

作者：Yongen Lin, Dagang Wang, Yue Meng, Wei Sun, Jianxiu Qiu, Wei Shangguan, Jingheng Cai, Yeonjoo Kim, Yongjiu Dai · 发表于：Journal of Hydrology Regional Studies · 年份：2023 · DOI：10.1016/j.ejrh.2023.101557 · 被引用次数：16 · 研究领域：Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies、Flood Risk Assessment and Management

Andun river basin of Southern China and 273 watersheds across the continental United States. Owing to the data incompleteness and the changing environment, there is bias existing in the data driven models for streamflow prediction, greatly limiting their application in actual practice. In this paper, we incorporate the bias learning components into the data driven models to establish the mapping-bias-learning models, and design two groups of experiments, one in the Andun River basin in China and the other in 273 watersheds in the continental United States (CONUS). In the first group of experiments, we respectively apply three machine learning algorithms and one traditional statistical method to generate sixteen mapping-bias-learning models ae well as four mapping-learning-alone models. We also explore the effectiveness of different bias learning strategies, including multiple bias learning, stacking bias learning and incremental updating bias learning. In the second group of experiments, we apply the mapping-bias-learning models to the streamflow prediction in 273 watersheds of CONUS to verify the universality and robustness of the bias learning method. The mapping-bias-learning models significantly outperform the mapping-learning-alone models, and the machine learning methods are superior to the traditional statistical method in term of the ability of bias learning. In addition, adopting the appropriate bias learning strategies can further improve the streamflow forecast per...