%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Abbas, A.
%A Baek, S.
%A Silvera, Norbert
%A Soulileuth, B.
%A Pachepsky, Y.
%A Ribolzi, Olivier
%A Boithias, L.
%A Cho, K. H.
%T In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models
%D 2021
%L fdi:010083788
%G ENG
%J Hydrology and Earth System Sciences
%@ 1027-5606
%K LAOS
%M ISI:000727690100001
%N 12
%P 6185-6202
%R 10.5194/hess-25-6185-2021
%U https://www.documentation.ird.fr/hor/fdi:010083788
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2022-01/010083788.pdf
%V 25
%W Horizon (IRD)
%X Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of E. coli in a 0.6 km(2) tropical headwater catchment located in the Lao People's Democratic Republic (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) methodology, 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 with 0.51 and 0.64 of the Nash-Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were -0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of -3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for E. coli fate and transport simulation at the catchment scale.
%$ 062 ; 038 ; 084 ; 020