教育背景

研究方向

基于物理模型与深度学习的水文模拟

学术成果

4. Li B, Sun T, Tian F, et al. Enhancing process-based hydrological models with embedded neural networks: A hybrid approach[J]. Journal of Hydrology, 2023: 130107.

3. Li B, Li R, Sun T, et al. Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau[J]. Journal of Hydrology, 2023, 620: 129401.

2. Li B, Zhou X, Ni G, et al. A multi-factor integrated method of calculation unit delineation for hydrological modeling in large mountainous basins[J]. Journal of Hydrology, 2021, 597(1–2):126180.

1. 李步, 田富强, 李钰坤, 倪广恒. 融合气象要素时空特征的深度学习水文模型[J]. 水科学进展, 2022, 33(6): 904-913.