Articles | Volume 25, issue 12
https://doi.org/10.5194/hess-25-6185-2021
https://doi.org/10.5194/hess-25-6185-2021
Research article
 | 
06 Dec 2021
Research article |  | 06 Dec 2021

In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models

Ather Abbas, Sangsoo Baek, Norbert Silvera, Bounsamay Soulileuth, Yakov Pachepsky, Olivier Ribolzi, Laurie Boithias, and Kyung Hwa Cho

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Cited articles

Abbasa, A., Baek, S., Kim M., Ligaray, M., Ribolzi, O., Silvera, N., Min, J.-H., Boithias, L., and Kyung, H. C.: Surface and sub-surface flow estimation at high temporal resolution using deep neural networks, J. Hydrol., 590, 125370, https://doi.org/10.1016/j.jhydrol.2020.125370, 2020. 
Abimbola, O. P., Mittelstet, A. R., Messer, T. L., Berry, E. D., Bartelt-Hunt, S. L., and Hansen, S. P.: Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern, Sci. Total Environ., 722, 137894, https://doi.org/10.1016/j.scitotenv.2020.137894, 2020. 
Abimbola, O., Mittelstet, A., Messer, T., Berry, E., and van Griensven, A.: Modeling and Prioritizing Interventions Using Pollution Hotspots for Reducing Nutrients, Atrazine and E. coli Concentrations in a Watershed, Sustainability, 13, 103, https://doi.org/10.3390/su13010103, 2021. 
Abadi, M., Barham, P., Chen, J., et al.: Kudlur, M.: Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), 265–283, Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, usenix The advanced computing systems association, Berkeley, California, United States, 2016. 
Ackerman, D. and Weisberg, S. B.: Evaluating HSPF runoff and water quality predictions at multiple time and spatial scales, edited by: SBW a. K. Miller, Southern California coastal water research project biennial report, 2006, 3535 Harbor Blvd., Suite 110 Costa Mesa, CA 92626, USA, 293–303, 2005. 
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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.
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