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Tang, Sijie; Wang, Shuo; Jiang, Jiping; Zheng, Yi (2025) Incorporating Causality Into Deep Learning Architectures to Improve Flash Drought Forecasts. Water Resources Research, 61 (10). doi:10.1029/2024wr039470

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Reference TypeJournal (article/letter/editorial)
TitleIncorporating Causality Into Deep Learning Architectures to Improve Flash Drought Forecasts
JournalWater Resources Research
AuthorsTang, SijieAuthor
Wang, ShuoAuthor
Jiang, JipingAuthor
Zheng, YiAuthor
Year2025 (October)Volume61
Issue10
PublisherAmerican Geophysical Union (AGU)
DOIdoi:10.1029/2024wr039470Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID19050503Long-form Identifiermindat:1:5:19050503:7
GUID0
Full ReferenceTang, Sijie; Wang, Shuo; Jiang, Jiping; Zheng, Yi (2025) Incorporating Causality Into Deep Learning Architectures to Improve Flash Drought Forecasts. Water Resources Research, 61 (10). doi:10.1029/2024wr039470
Plain TextTang, Sijie; Wang, Shuo; Jiang, Jiping; Zheng, Yi (2025) Incorporating Causality Into Deep Learning Architectures to Improve Flash Drought Forecasts. Water Resources Research, 61 (10). doi:10.1029/2024wr039470
In(2025, October) Water Resources Research Vol. 61 (10). American Geophysical Union (AGU)

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

Achiam J. (2023) arXiv preprint arXiv:2303.08774 Gpt‐4 technical report
()
Not Yet Imported: - book-chapter : 10.2134/agronmonogr60.2016.0034

If you would like this item imported into the Digital Library, please contact us quoting Book ID 9780891183587
Not Yet Imported: - journal-article : 10.1016/j.foreco.2009.09.001

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
Bai S. (2018) arXiv preprint arXiv:1803.01271 An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
()
Balles L. (2016) arXiv preprint arXiv:1612.05086 Coupling adaptive batch sizes with learning rates
Not Yet Imported: - book-chapter : 10.1007/978-3-642-35289-8_26

If you would like this item imported into the Digital Library, please contact us quoting Book ID 9783642352881
()
()
Bishop C. M. (2006) Pattern recognition and machine learning
()
Not Yet Imported: - journal-article : 10.1002/wat2.1520

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Buckingham E. (1904) Contributions to our knowledge of the aeration of soils
Not Yet Imported: - journal-article : 10.1371/journal.pone.0214508

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Not Yet Imported: Atmosphere - journal-article : 10.3390/atmos10090498

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
Devlin J. (2018) arXiv preprint arXiv:1810.04805 Bert: Pre‐training of deep bidirectional transformers for language understanding
Not Yet Imported: - journal-article : 10.1016/j.scitotenv.2020.142638

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
()
Not Yet Imported: - journal-article : 10.1016/j.agrformet.2017.08.031

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Frazier P. I. (2018) arXiv preprint arXiv:1807.02811 A tutorial on Bayesian optimization
()
()
Not Yet Imported: - journal-article : 10.3390/agriculture12010025

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Goodfellow I. (2016) Deep learning
Not Yet Imported: - journal-article : 10.1016/j.agwat.2022.108088

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Grus J. (2019) Data science from scratch: First principles with python
Not Yet Imported: - journal-article : 10.1109/jsen.2019.2923982

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
He F. (2019) Advances in Neural Information Processing Systems Control batch size and learning rate to generalize well: Theoretical and empirical evidence 32
He K. (2016) Proceedings of the IEEE conference on computer vision and pattern recognition
Not Yet Imported: - journal-article : 10.1093/qopen/qoad031

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1002/qj.3803

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1162/neco.1997.9.8.1735

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Hoedt P.‐J. Kratzert F. Klotz D. Halmich C. Holzleitner M. Nearing G. S. et al. (2021).Mc‐lstm: Mass‐conserving lstm. Paper presented at the International conference on machine learning.
()
()
Not Yet Imported: - journal-article : 10.1109/mcse.2007.55

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Hwang J. (2020) arXiv preprint arXiv:2007.06848 Modeling financial time series using LSTM with Trainable Initial Hidden States
Not Yet Imported: - book-chapter : 10.1016/B978-0-12-823457-0.00005-7

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.future.2019.10.026

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Javier P. (2021) Causal‐CCM a Python Implementation of Convergent Cross Mapping Causal‐CCM a python implementation of convergent cross mapping
()
Kingma D. P. (2014) arXiv preprint arXiv:1412.6980 Adam: A method for stochastic optimization
()
Lees T. (2021) Hydrology and Earth System Sciences Discussions Hydrological concept formation inside long short‐term memory (LSTM) networks 2021, 1
()
()
Not Yet Imported: - journal-article : 10.3390/rs16050780

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Li X. (2017) arXiv preprint arXiv:1702.01638 Concurrent activity recognition with multimodal CNN‐LSTM structure
()
Lorenz E. N. (1996) Pure and Applied Geophysics The essence of chaos 147, 598
Not Yet Imported: - journal-article : 10.1029/2021jf006490

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Lundberg S. (2017) arXiv preprint arXiv:1705.07874 A unified approach to interpreting model predictions
Not Yet Imported: - proceedings-article : 10.1109/JISIC.2014.50

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
Masson‐Delmotte V. (2021) Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change Climate change 2021: The physical science basis 2
()
Not Yet Imported: - journal-article : 10.1109/access.2022.3207287

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
Not Yet Imported: ACM Computing Surveys - journal-article : 10.1145/3649448

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Molnar C. (2020) Interpretable machine learning
()
()
Not Yet Imported: - journal-article : 10.1016/j.wace.2021.100321

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Osman M. (2020) Hydrology and Earth System Sciences Discussions Flash drought onset over the contiguous United States: Sensitivity of inventories and trends to quantitative definitions 2020, 1
()
()
()
Not Yet Imported: - journal-article : 10.1016/j.agrformet.2015.10.011

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Paszke A. (2019) Advances in Neural Information Processing Systems Pytorch: An imperative style, high‐performance deep learning library 32
Pitis S.(2016).Non‐zero initial states for recurrent neural networks. Retrieved fromhttps://r2rt.com/non‐zero‐initial‐states‐for‐recurrent‐neural‐networks.html
()
Powers D. M. (2020) arXiv preprint arXiv:2010.16061 Evaluation: From precision, recall and F‐measure to ROC, informedness, markedness and correlation
Prechelt L. (2002) Neural networks: Tricks of the trade , 55
()
()
()
Ronneberger O. (2015) Paper presented at the Medical image computing and computer‐assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5‐9, 2015, proceedings, part III 18 , 234
()
Sak H. (2014) arXiv preprint arXiv:1402.1128 Long short‐term memory based recurrent neural network architectures for large vocabulary speech recognition
Senay G. B. (2008) Mapping flash drought in the US: Southern Great Plains
()
Smith A. (2018) Climate Disasters NOAA national centers for environmental information (NCEI). US billion‐dollar weather and
Sundararajan M. Taly A. &Yan Q.(2017).Axiomatic attribution for deep networks. Paper presented at the International conference on machine learning.
()
Takens F. (2006) Dynamical systems and turbulence, Warwick 1980: Proceedings of a symposium held at the University of Warwick 1979/80 , 366
Not Yet Imported: Journal of Geophysical Research: Atmospheres - journal-article : 10.1029/2023jd039311

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: 2015 IEEE International Conference on Computer Vision (ICCV) - proceedings-article : 10.1109/ICCV.2015.510

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
Vaswani A. (2017) Advances in Neural Information Processing Systems Attention is all you need 30
Not Yet Imported: - book : 10.4324/9781315154282

If you would like this item imported into the Digital Library, please contact us quoting Book ID 9781315154282
Not Yet Imported: - journal-article : 10.1016/j.ecolmodel.2021.109692

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Not Yet Imported: - journal-article : 10.1016/j.wace.2023.100632

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
Not Yet Imported: - journal-article : 10.21105/joss.03021

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Wenke S. (2019) arXiv preprint arXiv:1902.03455 Contextual recurrent neural networks
White C. (2023) arXiv preprint arXiv:2301.08727 Neural architecture search: Insights from 1000 papers
()
Not Yet Imported: - journal-article : 10.1016/j.agwat.2024.108692

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
Not Yet Imported: - journal-article : 10.1016/j.ymssp.2020.106885

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
Yevjevich V. M. (1967) An objective approach to definitions and investigations of continental hydrologic droughts
()
()
Zhang A. (2021) arXiv preprint arXiv:2106.11342 Dive into deep learning
Not Yet Imported: Advances in Climate Change Research - journal-article : 10.1016/j.accre.2024.04.003

If you would like this item imported into the Digital Library, please contact us quoting Journal ID
()
()
()


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