Subject
Artificial Intelligence,Computer Science Applications,General Engineering
Reference78 articles.
1. Evaluation and interpretation of convolutional-recurrent networksfor regional hydrological modelling [Preprint];Anderson;Rivers and Lakes/Modelling approaches,2021
2. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors;Beck;Hydrology and Earth System Sciences,2021
3. Bennett, A., & Nijssen, B. (2021). Deep learned process parameterizations provide better representations of turbulent heat fluxes in hydrologic models. Water Resources Research, 57(5), e2020WR029328. https://doi.org/10.1029/2020WR029328.
4. AQ-Bench: A benchmark dataset for machine learning on global air quality metrics;Betancourt;Earth System Science Data,2021
5. Cao, B., Gruber, S., Zheng, D., & Li, X. (2020). The ERA5-Land Soil-Temperature Bias in Permafrost Regions [Preprint]. Frozen ground/Frozen Ground. https://doi.org/10.5194/tc-2020-76.