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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Bibliographic Details
Published in:	Geoscientific Model Development Vol. 15; no. 7; pp. 3021 - 3039
Main Authors:	Abbas, Ather, Boithias, Laurie, Pachepsky, Yakov, Kim, Kyunghyun, Chun, Jong Ahn, Cho, Kyung Hwa
Format:	Journal Article
Language:	English
Published:	Katlenburg-Lindau Copernicus GmbH 08.04.2022 European Geosciences Union Copernicus Publications
Subjects:	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
Online Access:	Get full text
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