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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Geoscientific Model Development Jg. 15; H. 7; S. 3021 - 3039
Hauptverfasser:	Abbas, Ather, Boithias, Laurie, Pachepsky, Yakov, Kim, Kyunghyun, Chun, Jong Ahn, Cho, Kyung Hwa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Katlenburg-Lindau Copernicus GmbH 08.04.2022 European Geosciences Union Copernicus Publications
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!