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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Summary: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.
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ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
1991-959X
DOI:10.5194/gmd-15-3021-2022