Probing the limit of hydrologic predictability with the Transformer network-Reference-Cited by-同舟云学术

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Probing the limit of hydrologic predictability with the Transformer network

Published:2024-06 Issue: Volume:637 Page:131389
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Liu Jiangtao,Bian Yuchen,Lawson Kathryn,Shen Chaopeng^ORCID

Publisher

Elsevier BV

Reference66 articles.

1. A novel application of transformer neural network (TNN) for estimating pan evaporation rate;Abed;Appl. Water Sci.,2022

2. A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations;Aboelyazeed;Biogeosciences,2023

3. Catchment Attributes and MEteorology for Large-Sample studies (CAMELS) version 2.0;Addor,2017

4. Groundwater estimation from major physical hydrology components using artificial neural networks and deep learning;Afzaal;Water,2020

5. Hydrological drought forecasting using a deep transformer model;Amanambu;Water,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain;Journal of Hydrology;2024-09

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