AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing wh...
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| Veröffentlicht in: | Geoscientific Model Development Jg. 15; H. 7; S. 3021 - 3039 |
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| Sprache: | Englisch |
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Katlenburg-Lindau
Copernicus GmbH
08.04.2022
European Geosciences Union Copernicus Publications |
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| ISSN: | 1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X, 1991-959X |
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| Abstract | Machine learning has shown great promise for simulating
hydrological phenomena. However, the development of machine-learning-based
hydrological models requires advanced skills from diverse fields, such as
programming and hydrological modeling. Additionally, data pre-processing and
post-processing when training and testing machine learning models are a
time-intensive process. In this study, we developed a python-based framework
that simplifies the process of building and training machine-learning-based
hydrological models and automates the process of pre-processing
hydrological data and post-processing model results. Pre-processing
utilities assist in incorporating domain knowledge of hydrology in the
machine learning model, such as the distribution of weather data into
hydrologic response units (HRUs) based on different HRU discretization
definitions. The post-processing utilities help in interpreting the model's
results from a hydrological point of view. This framework will help increase
the application of machine-learning-based modeling approaches in
hydrological sciences. |
|---|---|
| AbstractList | Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences. Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences. |
| Audience | Academic |
| Author | Cho, Kyung Hwa Pachepsky, Yakov Abbas, Ather Kim, Kyunghyun Chun, Jong Ahn Boithias, Laurie |
| Author_xml | – sequence: 1 givenname: Ather orcidid: 0000-0002-0031-745X surname: Abbas fullname: Abbas, Ather – sequence: 2 givenname: Laurie orcidid: 0000-0003-3414-7329 surname: Boithias fullname: Boithias, Laurie – sequence: 3 givenname: Yakov orcidid: 0000-0003-0232-6090 surname: Pachepsky fullname: Pachepsky, Yakov – sequence: 4 givenname: Kyunghyun surname: Kim fullname: Kim, Kyunghyun – sequence: 5 givenname: Jong Ahn orcidid: 0000-0001-8047-1811 surname: Chun fullname: Chun, Jong Ahn – sequence: 6 givenname: Kyung Hwa surname: Cho fullname: Cho, Kyung Hwa |
| BackLink | https://insu.hal.science/insu-03661482$$DView record in HAL |
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| Cites_doi | 10.1061/JRCEA4.0000287 10.1214/aos/1013203451 10.1016/j.trc.2020.102673 10.1162/neco.1997.9.8.1735 10.1007/s10994-006-6226-1 10.1111/jawr.12079 10.1038/s41598-020-80820-1 10.1145/3337821.3337892 10.1093/bioinformatics/17.6.520 10.1109/TKDE.2017.2720168 10.1007/s10208-009-9045-5 10.1016/j.watres.2019.115454 10.1016/j.ijforecast.2006.03.001 10.5194/hess-21-5293-2017 10.1016/j.neucom.2018.03.067 10.1007/978-3-030-28954-6_19 10.1109/34.709601 10.1080/00207543.2014.917771 10.1016/j.jhydrol.2020.125078 10.1029/2000JD900719 10.5194/hess-23-2647-2019 10.5194/gmd-2021-139 10.1016/j.atmosres.2012.11.003 10.1007/s00521-020-05010-6 10.1145/3380971 10.1016/j.jhydrol.2020.124901 10.1016/j.jhydrol.2020.125370 10.1002/2016WR019627 10.1007/978-3-030-26086-6_10 10.5194/gmd-14-1553-2021 10.1007/978-3-319-09235-5 10.1038/s41598-021-82891-0 10.3390/atmos10090555 10.1145/3292500.3330701 10.1038/s42256-019-0138-9 10.2166/wst.2020.369 10.1016/j.agwat.2020.106113 10.1145/2487575.2487629 10.5194/essd-13-4529-2021 10.1145/2939672.2939778 10.1016/j.jhydrol.2009.01.042 10.1038/s42256-019-0048-x 10.5194/hess-20-2611-2016 10.1016/j.ijforecast.2018.11.010 10.1016/j.scitotenv.2020.141107 10.5194/essd-13-3847-2021 10.1145/2939672.2939785 10.2134/jeq2017.11.0456 10.1016/j.ijforecast.2021.03.012 10.1016/0022-1694(83)90177-4 10.5194/essd-12-2459-2020 10.1111/gwat.12925 10.1002/hyp.14126 10.24963/ijcai.2017/366 10.1006/jcss.1997.1504 10.1016/j.watres.2021.117001 |
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| Snippet | Machine learning has shown great promise for simulating
hydrological phenomena. However, the development of machine-learning-based
hydrological models requires... Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires... |
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| SubjectTerms | Algorithms Analysis Business metrics Datasets Deep learning Hydrologic data Hydrologic models Hydrologic processes Hydrology Learning algorithms Libraries Machine learning Meteorological data Methods Modelling Neural networks Optimization Sciences of the Universe Testing equipment Time series Training Utilities Visualization |
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| Title | AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods |
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