Preprints
https://doi.org/10.5194/hess-2021-98
https://doi.org/10.5194/hess-2021-98

  08 Apr 2021

08 Apr 2021

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

In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models

Ather Abbas1, Sangsoo Baek1, Norbert Silvera2, Bounsamay Soulileuth3, Yakov Pachepsky4, Olivier Ribolzi5, Laurie Boithias5, and Kyung Hwa Cho1 Ather Abbas et al.
  • 1School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
  • 2Institute of Ecology and Environmental Sciences of Paris (iEES-Paris), Sorbonne Université, Univ Paris Est Creteil, IRD, CNRS, INRA, Paris, France
  • 3IRD, IEES-Paris UMR 242, c/o National Agriculture and Forestry Research Institute, Vientiane, Lao PDR
  • 4Environmental Microbial and Food Safety Laboratory, USDA-ARS, Beltsville, MD, USA
  • 5Géosciences Environnement Toulouse, Université de Toulouse, CNRS, IRD, UPS, Toulouse, France

Abstract. Contamination of surface waters through microbiological pollutants is a major concern to public health. Although long-term and high-frequency E. coli monitoring can help prevent diseases from fecal pathogenic microorganisms, this monitoring is time consuming and expensive. Process-driven models are an alternative method for determining fecal pathogenic microorganisms. However, process-based modeling still has limitations in improving the model accuracy because of the complex mechanistic relationships among hydrological and environmental variables. On the other hand, with the rise in data availability and computation power, the use of data-driven models is increasing. Therefore, in this study, we simulated the transport of Escherichia coli (E. coli) in a 0.6 km² tropical headwater catchment located in Lao PDR using a deep learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) technique, whereas the process-based model was constructed using the Hydrological Simulation Program–FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the E. coli transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow, by showing 0.51 and 0.64 of Nash–Sutcliffe Efficiency (NSE), respectively, whereas the NSE values yielded by the HSPF were −0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentration from LSTM also improved, yielding an NSE of 0.35, whereas the HSPF showed an unacceptable performance, with an NSE value of −3.01. This is because of the limitation of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed rise and drop patterns corresponding to annual changes in land use. This study shows the application of deep learning-based models as an efficient alternative to process-based models for E. coil fate and transport simulation at the catchment scale.

Ather Abbas et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-98', Anonymous Referee #1, 21 Apr 2021
  • RC2: 'Comment on hess-2021-98', Anonymous Referee #2, 23 Jul 2021

Ather Abbas et al.

Ather Abbas et al.

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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 literature. We compared the performance of a process driven (HSPF) and a data-driven (LSTM) model for E. coli simulation. We show that LSTM can be an alternative to process driven models for estimation of E. coli in surface waters.