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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Vydáno v:	Geoscientific Model Development Ročník 15; číslo 7; s. 3021 - 3039
Hlavní autoři:	Abbas, Ather, Boithias, Laurie, Pachepsky, Yakov, Kim, Kyunghyun, Chun, Jong Ahn, Cho, Kyung Hwa
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Katlenburg-Lindau Copernicus GmbH 08.04.2022 European Geosciences Union Copernicus Publications
Témata:	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
On-line přístup:	Získat plný text
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