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.
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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EGUsphere, https://doi.org/10.5194/egusphere-2025-4138, https://doi.org/10.5194/egusphere-2025-4138, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
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Cattle ponds are commonly used for cooling livestock and for irrigation. Levels of bacteria Escherichia coli in water characterize water quality. We developed the model of fate and transport of E. coli using the HydroGeoshere modeling platform. Pond surface was imaged to estimate the E. coli load to the pond from animals in water. This work shows the opportunity and the approach to obtaining a moderately accurate forecast of microbial water quality in cattle ponds using readily available data.
Laurie Boithias, Olivier Ribolzi, Emma Rochelle-Newall, Chanthanousone Thammahacksa, Paty Nakhle, Bounsamay Soulileuth, Anne Pando-Bahuon, Keooudone Latsachack, Norbert Silvera, Phabvilay Sounyafong, Khampaseuth Xayyathip, Rosalie Zimmermann, Sayaphet Rattanavong, Priscia Oliva, Thomas Pommier, Olivier Evrard, Sylvain Huon, Jean Causse, Thierry Henry-des-Tureaux, Oloth Sengtaheuanghoung, Nivong Sipaseuth, and Alain Pierret
Earth Syst. Sci. Data, 14, 2883–2894, https://doi.org/10.5194/essd-14-2883-2022, https://doi.org/10.5194/essd-14-2883-2022, 2022
Short summary
Short summary
Fecal pathogens in surface waters may threaten human health, especially in developing countries. The Escherichia coli (E. coli) database is organized in three datasets and includes 1602 records from 31 sampling stations located within the Mekong River basin in Lao PDR. Data have been used to identify the drivers of E. coli dissemination across tropical catchments, including during floods. Data may be further used to interpret new variables or to map the health risk posed by fecal pathogens.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
Short summary
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.
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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....