Submitted as: development and technical paper 17 Jun 2021

Submitted as: development and technical paper | 17 Jun 2021

Review status: this preprint is currently under review for the journal GMD.

AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods

Ather Abbas1, Laurie Boithias2, Yakov Pachepsky3, Kyunghyun Kim4, Jong Ahn Chun5, and Kyung Hwa Cho1 Ather Abbas et al.
  • 1Urban and Environmental Engineering, Ulsan national institute of science and technology, Ulsan, Republic of Korea
  • 2Geosciences Environnement Toulouse, Université de Toulouse, CNRS, IRD, UPS, 31400 Toulouse, France
  • 3Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD, USA
  • 4Watershed and Total Load Management Research Division, National Institute of Environmental Research, Hwangyeong-ro 42, Seogu, Incheon 22689, Republic of Korea
  • 5APEC Climate Center, Climate Research Department, Busan, Republic of Korea

Abstract. 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 is 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 of hydrological data and post-processing of 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.

Ather Abbas et al.

Status: open (until 27 Aug 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Ather Abbas et al.

Data sets

AI4Water: a framework for building and testing machine learning models for hydrological simulations Ather Abbas

Model code and software

Al4Water Ather Abbas

Ather Abbas et al.


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Short summary
The field of artificial intelligence has shown promising results in wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques requires expertise from diverse fields. In this study, we developed an open source framework based upon python programming language to simplify the process of development of hydrological models of time series data using machine learning.