Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3021-2022
https://doi.org/10.5194/gmd-15-3021-2022
Development and technical paper
 | 
08 Apr 2022
Development and technical paper |  | 08 Apr 2022

AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods

Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho

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Cited articles

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M.: Tensorflow: A system for large-scale machine learning, in: 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), 265–283, 2016. 
Abbas, A., Baek, S., Kim, M., Ligaray, M., Ribolzi, O., Silvera, N., Min, J.-H., Boithias, L., and Cho, K. H.: Surface and sub-surface flow estimation at high temporal resolution using deep neural networks, J. Hydrol., 590, 125370, https://doi.org/10.1016/j.jhydrol.2020.125370, 2020. 
Abbas, A., Iftikhar, S., and Kwon, D.: AtrCheema/AI4Water: AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods (v1.0-beta.1), Zenodo [data set and code], https://doi.org/10.5281/zenodo.5595680, 2021. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M.: Optuna: A next-generation hyperparameter optimization framework, in: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, 2623–2631, 2019. 
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Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
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