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!
Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
1991-959X
DOI:10.5194/gmd-15-3021-2022