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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Vydané v:	Geoscientific Model Development Ročník 15; číslo 7; s. 3021 - 3039
Hlavní autori:	Abbas, Ather, Boithias, Laurie, Pachepsky, Yakov, Kim, Kyunghyun, Chun, Jong Ahn, Cho, Kyung Hwa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Katlenburg-Lindau Copernicus GmbH 08.04.2022 European Geosciences Union Copernicus Publications
Predmet:	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
ISSN:	1991-9603, 1991-959X, 1991-962X, 1991-9603, 1991-962X, 1991-959X
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