References#
This page collects the bibliography used throughout the GeoPrior-v3 documentation.
The references listed here support the scientific foundations, workflow design, uncertainty analysis, and application context discussed across the documentation. They are maintained centrally in the project BibTeX database:
docs/source/references.bib
How references are used#
Citations are inserted throughout the documentation using Sphinx BibTeX roles such as:
:cite:p:`some_key`
:cite:t:`some_key`
This page renders the full bibliography so readers can see all cited works in one place.
Scope#
The bibliography may include references related to:
subsidence and groundwater physics,
poroelasticity and consolidation,
physics-informed neural networks,
time-series forecasting,
uncertainty quantification and calibration,
geospatial and environmental applications,
benchmarking and reproducibility.
Notes for contributors#
When adding a new scientific claim, method comparison, or historical background point that depends on prior work, add the corresponding BibTeX entry to:
docs/source/references.bib
and cite it in the relevant page instead of pasting raw bibliographic text directly into the prose.
A good practice is:
keep citation keys stable,
use complete bibliographic metadata,
and prefer high-quality primary sources when possible.
Full bibliography#
Bryan Lim, Sercan O. Arik, Nicolas Loeff, and Tomas Pfister. Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4):1748–1764, 2021. doi:10.1016/j.ijforecast.2021.03.012.
K. L. Kouadio, Z. Liu, R. Liu, P. C. Bizimana, G. Yang, and W. Liu. Xtft: a next-generation temporal fusion transformer for uncertainty-rich time series forecasting. 2025. Preprint, submitted to IEEE TPAMI. URL: https://authorea.com/users/643438/articles/711823-xtft-a-next-generation-temporal-fusion-transformer-for-uncertainty-rich-time-series-forecasting, doi:10.22541/au.175390529.91420978.v1.
James Donnelly, Alireza Daneshkhah, and Soroush Abolfathi. Physics-informed neural networks as surrogate models of hydrodynamic simulators. Science of The Total Environment, 912:168814, 11 2023. doi:10.1016/j.scitotenv.2023.168814.
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, and Dakshina M. Valiveti. Physics-informed neural networks for heterogeneous poroelastic media. International Journal for Computational Methods in Engineering Science and Mechanics, 26(2):187–207, 2024. doi:10.1080/15502287.2024.2440420.
Berenice Zapata-Norberto, Eric Morales-Casique, and Graciela S. Herrera. One-dimensional simulation of land subsidence in vertically-heterogeneous highly compressible aquitards coupled with data assimilation via ensemble kalman filter. Environmental Modelling & Software, 194:106690, 2025. doi:10.1016/j.envsoft.2025.106690.
Devin L Galloway and Thomas J Burbey. Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8):1459, 2011. doi:10.1007/s10040-011-0775-5.
Jörn Hoffmann, S. A. Leake, D. L. Galloway, and Alica M. Wilson. MODFLOW-2000 Ground-Water Model — User Guide to the Subsidence and Aquifer-System Compaction (SUB) Package. U.S. Geological Survey (USGS), 2000. URL: https://pubs.usgs.gov/of/2003/ofr03-233/pdf/ofr03233.pdf.
J. Ellis, J. E. Knight, J. T. White, M. Sneed, J. D. Hughes, J. K. Ramage, C. L. Braun, A. Teeple, L. K. Foster, S. H. Rendon, and others. Hydrogeology, land-surface subsidence, and documentation of the gulf coast land subsidence and groundwater-flow (gulf) model, southeast texas, 1897–2018. Technical Report, U.S. Geological Survey, 2023. doi:10.3133/pp1877.
L. Schreyer Bennethum, M. A. Murad, and J. H. Cushman. Modified darcy's law, terzaghi's effective stress principle and fick's law for swelling clay soils. Computers and Geotechnics, 20(3):245–266, 1997. Theoretical and Experimental Methods for Particulate Materials. doi:10.1016/S0266-352X(97)00005-0.
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, and Dakshina Murthy Valiveti. Physics-informed neural networks for heterogeneous poroelastic media. arXiv preprint arXiv:2407.03372, 2024. arXiv:2407.03372.
Md Fahim Hasan, Ryan Smith, Sanaz Vajedian, Rahel Pommerenke, and Sayantan Majumdar. Global land subsidence mapping reveals widespread loss of aquifer storage capacity. Nature Communications, 14(1):1–10, 2023. doi:10.1038/s41467-023-41933-z.
Mehdi Bagheri-Gavkosh, Seiyed Mossa Hosseini, Behzad Ataie-Ashtiani, Yasamin Sohani, Homa Ebrahimian, Faezeh Morovat, and Shervin Ashrafi. Land subsidence: a global challenge. Science of the Total Environment, 2021. doi:10.1016/j.scitotenv.2021.146193.
A. K. Sarma, S. Roy, C. Annavarapu, P. Roy, and S. Jagannathan. Interface pinns (i-pinns): a physics-informed neural networks framework for interface problems. Computer Methods in Applied Mechanics and Engineering, 429:117135, 2024. doi:10.1016/j.cma.2024.117135.
Manoochehr Shirzaei, Jeffrey Freymueller, Torbjörn E Törnqvist, Devin L Galloway, Tina Dura, and Philip S J Minderhoud. Measuring, modelling and projecting coastal land subsidence. Nature Reviews Earth & Environment, 2(1):40–58, 2021. URL: https://doi.org/10.1038/s43017-020-00115-x, doi:10.1038/s43017-020-00115-x.
J. D. Hunter. Matplotlib: a 2d graphics environment. Computing in Science & Engineering, 9(3):90–95, 2007. doi:10.1109/MCSE.2007.55.
Keras Team. Serialization and saving. 2026. URL: https://keras.io/guides/serialization_and_saving/.
Python Software Foundation. Dataclasses — data classes. 2026. URL: https://docs.python.org/3/library/dataclasses.html.
Maziar Raissi, Paris Perdikaris, and George E. Karniadakis. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378:686–707, 2019. doi:10.1016/j.jcp.2018.10.045.
Keras Team. The compile() method: metrics routing. 2026. URL: https://keras.io/api/models/model_training_apis/.
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Keras Team. Kerastuner documentation. 2026. URL: https://keras.io/keras_tuner/.
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Jacob Bear. Dynamics of Fluids in Porous Media. Dover Publications, 1972.
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Kouao Laurent Kouadio, Rong Liu, Shiyu Jiang, Zhuo Liu, Serge Kouamelan, Wenxiang Liu, Zhanhui Qing, and Zhiwen Zheng. Physics-informed deep learning reveals divergent urban land subsidence regimes. Unpublished manuscript. Submitted to Nature Communications, 2025.