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A demo of using CGAN to generate cross-section using accessible parameters

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cstools

https://github.com/VMerwadeGroup/cstools

Data

Please download data using the link: https://www.hydroshare.org/resource/7e5af473a4d448a4ae5fc5163bee89c2

The data should be put in ./data/Wabash/feature, or replace the input_dir in ./code/wabash.ipynb with the desired path.

Citation

If you find the article or code useful for your project, please refer to

Liang, C.-Y., & Merwade, V. (2026). Predicting river bathymetry in data sparse regions using a generative deep learning model. Journal of Hydrology, 664, 134450. https://doi.org/10.1016/j.jhydrol.2025.134450

@article{Liang2026, author = {Chung-Yuan Liang and Venkatesh Merwade}, doi = {10.1016/j.jhydrol.2025.134450}, issn = {00221694}, journal = {Journal of Hydrology}, month = {1}, pages = {134450}, title = {Predicting river bathymetry in data sparse regions using a generative deep learning model}, volume = {664}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169425017901}, year = {2026} }

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