Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-6185-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Dec 2021
Research article |
| 06 Dec 2021
| 06 Dec 2021
In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models
Ather Abbas
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan 689-798, Republic of Korea
Sangsoo Baek
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan 689-798, Republic of Korea
Norbert Silvera
Institute of Ecology and Environmental Sciences of Paris
(iEES-Paris), Sorbonne Université, Univ. Paris Est Creteil, IRD, CNRS, INRA, Paris, France
Bounsamay Soulileuth
IRD, IEES-Paris UMR 242, c/o National Agriculture and Forestry
Research Institute, Vientiane, Lao PDR
Yakov Pachepsky
Environmental Microbial and Food Safety Laboratory, USDA-ARS,
Beltsville, MD, USA
Olivier Ribolzi
Géosciences Environnement Toulouse, Université de Toulouse,
CNRS, IRD, UPS, Toulouse, France
Laurie Boithias
CORRESPONDING AUTHOR
Géosciences Environnement Toulouse, Université de Toulouse,
CNRS, IRD, UPS, Toulouse, France
Kyung Hwa Cho
CORRESPONDING AUTHOR
School of Urban and Environmental Engineering, Ulsan National
Institute of Science and Technology, Ulsan 689-798, Republic of Korea
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Cited
11 citations as recorded by crossref.
- Escherichia coli 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
- Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea S. Suh et al. 10.1016/j.scitotenv.2024.177459
- Developing a data-driven modeling framework for simulating a chemical accident in freshwater S. Kim et al. 10.1016/j.jclepro.2023.138842
- Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database M. Ayach et al. 10.3390/resources12120143
- Patterns and drivers of fecal coliform exports in a typhoon-affected watershed: insights from 10-year observations and SWAT model Z. Xie et al. 10.1016/j.jclepro.2023.137044
- Development of a E. coli Simulation Module for a Watershed Model STREAM T. Lee et al. 10.7846/JKOSMEE.2023.26.3.225
- Groundwaters in the Auvergne-Rhône-Alpes Region, France: Grouping Homogeneous Groundwater Bodies for Optimized Monitoring and Protection M. Ayach et al. 10.3390/w16060869
- Modeling the fate and transport of E. coli pathogens in the Tano River Basin of Ghana under climate change and socioeconomic scenarios S. Yeboah et al. 10.1007/s11356-024-35123-7
- Water quality analysis based on LSTM and BP optimization with a transfer learning model Q. Luo et al. 10.1007/s11356-023-31068-5
- The Multi-Parameter Mapping of Groundwater Quality in the Bourgogne-Franche-Comté Region (France) for Spatially Based Monitoring Management A. Bousouis et al. 10.3390/su16198503
- Discrimination of Spatial and Temporal Variabilities in the Analysis of Groundwater Databases: Application to the Bourgogne-Franche-Comté Region, France A. Bousouis et al. 10.3390/w17030384
11 citations as recorded by crossref.
- Escherichia coli 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
- Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea S. Suh et al. 10.1016/j.scitotenv.2024.177459
- Developing a data-driven modeling framework for simulating a chemical accident in freshwater S. Kim et al. 10.1016/j.jclepro.2023.138842
- Multi-Parameter Analysis of Groundwater Resources Quality in the Auvergne-Rhône-Alpes Region (France) Using a Large Database M. Ayach et al. 10.3390/resources12120143
- Patterns and drivers of fecal coliform exports in a typhoon-affected watershed: insights from 10-year observations and SWAT model Z. Xie et al. 10.1016/j.jclepro.2023.137044
- Development of a E. coli Simulation Module for a Watershed Model STREAM T. Lee et al. 10.7846/JKOSMEE.2023.26.3.225
- Groundwaters in the Auvergne-Rhône-Alpes Region, France: Grouping Homogeneous Groundwater Bodies for Optimized Monitoring and Protection M. Ayach et al. 10.3390/w16060869
- Modeling the fate and transport of E. coli pathogens in the Tano River Basin of Ghana under climate change and socioeconomic scenarios S. Yeboah et al. 10.1007/s11356-024-35123-7
- Water quality analysis based on LSTM and BP optimization with a transfer learning model Q. Luo et al. 10.1007/s11356-023-31068-5
- The Multi-Parameter Mapping of Groundwater Quality in the Bourgogne-Franche-Comté Region (France) for Spatially Based Monitoring Management A. Bousouis et al. 10.3390/su16198503
- Discrimination of Spatial and Temporal Variabilities in the Analysis of Groundwater Databases: Application to the Bourgogne-Franche-Comté Region, France A. Bousouis et al. 10.3390/w17030384
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Short summary
Correct estimation of fecal indicator bacteria in surface waters is critical for public health. Process-driven models and recently data-driven models have been applied for water quality modeling; however, a systematic comparison for simulation of E. coli is missing in the literature. We compared performance of process-driven (HSPF) and data-driven (LSTM) models for E. coli simulation. We show that LSTM can be an alternative to process-driven models for estimation of E. coli in surface waters.
Correct estimation of fecal indicator bacteria in surface waters is critical for public health....
