Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-3021-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-3021-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 Apr 2022
Development and technical paper |
| 08 Apr 2022
| 08 Apr 2022
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Ather Abbas
Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
Laurie Boithias
Géosciences Environnement Toulouse, Université de Toulouse, CNRS, IRD, UPS, 31400 Toulouse, France
Yakov Pachepsky
Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD, USA
Kyunghyun Kim
Watershed and Total Load Management Research Division, National Institute of Environmental Research, Hwangyeong-ro 42, Seogu, Incheon 22689, Republic of Korea
Climate Research Department, APEC Climate Center, Busan, Republic of Korea
Kyung Hwa Cho
CORRESPONDING AUTHOR
Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea
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- Prediction and interpretation of antibiotic-resistance genes occurrence at recreational beaches using machine learning models S. Iftikhar et al. 10.1016/j.jenvman.2022.116969
- Streamflow prediction model for agriculture dominated tropical watershed using machine learning and hierarchical predictor selection algorithms G. Kartick et al. 10.1016/j.ejrh.2024.101895
- Application of Decision-Tree-Based Machine Learning Algorithms for Prediction of Antimicrobial Resistance M. Yasir et al. 10.3390/antibiotics11111593
- Digital imaging-in-flow (FlowCAM) and probabilistic machine learning to assess the sonolytic disinfection of cyanobacteria in sewage wastewater Z. Jaffari et al. 10.1016/j.jhazmat.2024.133762
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- An open-source deep learning model for predicting effluent concentration in capacitive deionization M. Son et al. 10.1016/j.scitotenv.2022.159158
- Adsorption of Cr(VI) ions onto fluorine-free niobium carbide (MXene) and machine learning prediction with high precision R. Ishtiaq et al. 10.1016/j.jece.2024.112238
- Artificial neural networks for insights into adsorption capacity of industrial dyes using carbon-based materials S. Iftikhar et al. 10.1016/j.seppur.2023.124891
- Deep learning for monthly rainfall–runoff modelling: a large-sample comparison with conceptual models across Australia S. Clark et al. 10.5194/hess-28-1191-2024
- Antibiotic resistance genes prevalence prediction and interpretation in beaches affected by urban wastewater discharge Q. Zahra et al. 10.1016/j.onehlt.2023.100642
- Machine learning-enhanced photocatalysis for environmental sustainability: Integration and applications A. Jaison et al. 10.1016/j.mser.2024.100880
- B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology A. Amaranto & M. Mazzoleni 10.1016/j.envsoft.2022.105609
- Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green Z. Jaffari et al. 10.1016/j.jhazmat.2022.130031
- Using statistical and machine learning approaches to describe estuarine tidal dynamics F. Lauer & F. Kösters 10.2166/hydro.2024.294
- Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents Z. Jaffari et al. 10.1016/j.jhazmat.2023.132773
- <i>Escherichia coli</i> concentration, multiscale monitoring over the decade 2011–2021 in the Mekong River basin, Lao PDR L. Boithias et al. 10.5194/essd-14-2883-2022
- Long short-term memory models of water quality in inland water environments J. Pyo et al. 10.1016/j.wroa.2023.100207
- Machine learning analysis to interpret the effect of the photocatalytic reaction rate constant (k) of semiconductor-based photocatalysts on dye removal C. Kim et al. 10.1016/j.jhazmat.2023.132995
Latest update: 18 Nov 2024
Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
The field of artificial intelligence has shown promising results in a wide variety of fields...
