참고문헌
- Bauer, P. et al., 2021, The digital revolution of Earth-system science. Nat. Comput. Sci. 1, 104-113. https://doi.org/10.1038/s43588-021-00023-0
- Castangia et al., 2023, Transformer neural networks for interpretable flood forecasting, Environmental Modelling & Software, 160, 105581.
- Espeholt, L. et al., 2022, Deep learning for twelve hour precipitation forecasts. Nat. Commun. 13, 5145.
- Feng, D., Tan, Z., and He, Q.Z., 2023, Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model. Water Resources Research, 59.
- Hess et al., 2022, Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nat. Mach. Intell. 4, 828-839. https://doi.org/10.1038/s42256-022-00540-1
- Hosseiny, H., Nazari, F., Smith, V., and Nataraj, C., 2020, A framework for modeling flood depth using a hybrid of hydraulics and machine learning. Sci. Rep. 10, 8222.
- Irrgang, C. et al., 2021, Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nat. Mach. Intell. 3, 667-674. https://doi.org/10.1038/s42256-021-00374-3
- Kadow, C., Hall, D. M. and Ulbrich, U., 2020, Artificial intelligence reconstructs missing climate information. Nat. Geosci. 13, 408-413. https://doi.org/10.1038/s41561-020-0582-5
- Koppa, A., Rains, D., Hulsman, P., Poyatos, R., and Miralles, D. G. 2022, A deep learning-based hybrid model of global terrestrial evaporation. Nat. Commun. 13, 1912.
- Larson, A., 2022, A clearer view of Earth's water cycle via neural networks and satellite data. Nat. Rev. Earth Environ. 3, 361.
- Nearing et al., 2020. A deep learning architecture for conservative dynamical systems: Application to rainfall-runoff modeling. In AI for Earth Sciences Workshop at NEURIPS.
- Pekel, J.-F, Cottam, A., Gorelick, N. & Belward, A. S. High resolution mapping of global surface water and its long-term changes. Nature 540, 418-422 (2016). https://doi.org/10.1038/nature20584
- Richards, C.E., Tzachor, A., Avin, S., and Fenner, R., 2023. Rewards, risks and responsible deployment of artificial intelligence in water systems. Nature Water, 1, 422-432.
- Sharafati, A., Asadollah, S. B. H. S., and Neshat, A., 2020, A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. J. Hydrol. 591, 125468.
- Sit et al., 2020, A comprehensive review of deep learning applications in hydrology and water resources, Water Science and Technology, 82 (12): 2635-2670. https://doi.org/10.2166/wst.2020.369
- Vaswani et al., 2017, Attention is all you need. Advances in neural information processing systems.
- Xu et al., 2023, Transformer Based Water Level Prediction in Poyang Lake, China. Water, 15, 576.
- Zaniolo, M., Giuliani, M., Sinclair, S., Burlando, P., and Castelletti, A., 2021, When timing matters-misdesigned dam filling impacts hydropower sustainability. Nat. Commun. 12, 3056.
- Zarei, M. et al., 2021, Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages. Sci. Rep. 11, 24295.
- Zeng, A., Chen, M., Zhang, L., and Xu, Q. 2023, Are Transformers Effective for Time Series Forecasting?, Proceedings of the AAAI Conference on Artificial Intelligence