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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This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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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
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Short summary
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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
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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.
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.
Adomat, Y., Orzechowski, G. H., Pelger, M., Haas, R., Bartak, R.,
Nagy-Kovács, Z. Á., Appels, J., and Grischek, T.: New Methods for
Microbiological Monitoring at Riverbank Filtration Sites, Water, 12, 584, https://doi.org/10.3390/w12020584, 2020.
Ahmadisharaf, E. and Benham, B. L.: Risk-based decision making to evaluate
pollutant reduction scenarios, Sci. Total Environ., 702, 135022,
https://doi.org/10.1016/j.scitotenv.2019.135022, 2020.
Ahmed, S. I., Singh, A., Rudra, R., and Gharabaghi, B.: Comparison of CANWET
and HSPF for water budget and water quality modeling in rural Ontario,
Water Qual. Res. J. Can., 49, 53–71, 2014.
Anderson, S. and Radic, V.: Evaluation and interpretation of convolutional-recurrent networks for regional hydrological modelling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-113, in review, 2021.
Banhatti, A. G. and Deka, P. C.: Effects of Data Pre-processing on the
Prediction Accuracy of Artificial Neural Network Model in Hydrological Time
Series, in: Urban Hydrology, Watershed Management and Socio-Economic
Aspects, Springer, Heidelberg, Germany, 265–275, 2016.
Bain, R. E., Wright, J. A., Christenson, E., and Bartram, J.: Rural: urban
inequalities in post 2015 targets and indicators for drinking-water, Sci. Total. Environ., 490, 509–513, 2014.
Bengio, Y., Lecun, Y., and Hinton, G.: Deep learning for AI, Commun. ACM, 64, 58–65, 2021.
Benham, B., Yagow, G., Barham, B., Zeckoski, R., and Dillaha, T.: Total
Maximum Daily Load Development: Mill Creek bacteria (E. coli) impairment,
Page County, Virginia, Richmond, VA, USA, Virginia Department of Environmental
Quality, 2005.
Bicknell, B. R., Imhoff, J. C., Kittle Jr., J. L., Donigian Jr., A. S., and
Johanson, R. C.: Hydrological simulation program – FORTRAN user's manual for
version 11, Environmental Protection Agency Report No. EPA/600/R-97/080, US Environmental Protection Agency, Athens, GA, USA, 1997.
Boithias, L., Choisy, M., Souliyaseng, N., Jourdren, M., Quet, F., Buisson,
Y., Thammahacksa, C., Silvera, N., Latsachack, K., Sengtaheuanghoung, O., Pierret, A., Rochelle-Newall, E., Becerra, S., and Ribolzi, O.: Hydrological regime and water shortage as drivers
of the seasonal incidence of diarrheal diseases in a tropical montane
environment, PLOS Neglect. Trop. D., 10, e0005195. https://doi.org/10.1371/journal.pntd.0005195,
2016.
Boithias, L., Auda, Y., Audry, S., Bricquet, J. P., Chanhphengxay, A., Chaplot, V., de Rouw, A., Henry des Tureaux, T., Huon, S., and Janeau, J.
l.: The Multiscale TROPIcal CatchmentS critical zone observatory M-TROPICS
dataset II: land use, hydrology and sediment production monitoring in Houay
Pano, northern Lao PDR, Hydrol. Proc., 35, e14126, https://doi.org/10.1002/hyp.14126, 2021a.
Boithias, L., Ribolzi, O., Lacombe, G., Thammahacksa, C., Silvera, N.,
Latsachack, K., Soulileuth, B., Viguier, M., Auda, Y., and Robert, E.:
Quantifying the effect of overland flow on Escherichia coli pulses during
floods: use of a tracer-based approach in an erosion-prone tropical
catchment, J. Hydrol., 594, 125935, https://doi.org/10.1016/j.jhydrol.2020.125935, 2021b.
Boithias, L., Ribolzi, O., Phachomphon, K., Phommasack, T., Valentin, C., and Sipaseuth, N.: Sub-catchments boundaries of the Houay Pano catchment, northern Lao PDR [Data set], https://doi.org/10.23708/M8NJA0, 2021c.
Causse, J., Billen, G., Garnier, J., Henri-des-Tureaux, T., Olasa, X.,
Thammahacksa, C., Latsachakd, K. O., Soulileuthd, B., Sengtaheuanghounge, O., Rochelle-Newall, E., and Ribolzi, O.: Field and modelling studies of
Escherichia coli loads in tropical streams of montane agroecosystems,
J. Hydro.-Environ. Res., 9, 496–507, https://doi.org/10.1016/j.jher.2015.03.003, 2015.
Chanhphengxay, A., Phommasack, T., and Valentin, C.: Soil map of the Houay Pano catchment, northern Lao PDR (1998) [Data set], https://doi.org/10.23708/FFEDIR, 2021.
Chen, H. J. and Chang, H.: Response of discharge, TSS, and E. coli to
rainfall events in urban, suburban, and rural watersheds, Environmental Science: Processes & Impacts, 16, 2313–2324,
2014.
Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., Liu, F., Zuo, M., Zoua, X., Wang, J., Zhang, Y., Chen, D., Chen, X., Deng, Y., and Renc, H.: Comparative analysis of surface water quality prediction performance and
identification of key water parameters using different machine learning
models based on big data, Water Res., 171, 115454,
https://doi.org/10.1016/j.watres.2019.115454, 2020.
Chin, D. A., Sakura-Lemessy, D., Bosch, D. D., and Gay, P. A.:
Watershed-scale fate and transport of bacteria, T. ASABE, 52, 145–154, https://doi.org/10.13031/2013.25955, 2009.
Cho, K. H., Pachepsky, Y. A., Kim, J. H., Guber, A. K., Shelton, D. R., and
Rowland, R.: Release of Escherichia coli from the bottom sediment in a
first-order creek: Experiment and reach-specific modeling, J. Hydrol., 391, 322–332,
https://doi.org/10.1016/j.jhydrol.2010.07.033, 2010.
Cho, K. H., Pachepsky, Y. A., Oliver, D. M., Muirhead, R. W., Park, Y.,
Quilliam, R. S., and Shelton, D. R.:. Modeling fate and transport of
fecally-derived microorganisms at the watershed scale: state of the science
and future opportunities, Water Res., 100, 38–56,
https://doi.org/10.1016/j.watres.2016.04.064, 2016.
Chuang, C. C., Wang, C. M., and Li, C. W.: Weighted linear regression for
symbolic interval-values data with outliers, in: 2010 5th IEEE Conference on Industrial Electronics and Applications, IEEE, 2238–2242, 15–17 June 2010, Taichung, Taiwan, https://doi.org/10.1109/ICIEA.2010.5515157, 2010.
Clevert, D. A., Unterthiner, T., and Hochreiter, S.: Fast and accurate deep
network learning by exponential linear units (elus), arXiv [preprint], arXiv:1511.07289, 2015.
Chollet, F.: Deep learning with Python, Simon and Schuster, Manning Publications Co, 20 Baldwin Road, P.O. Box 761, Shelter Island, NY 11964, USA, ISBN 9781617294433, 2018.
Dosovitskiy, A. and Djolonga, J.: You Only Train Once: Loss-Conditional
Training of Deep Networks, in: International Conference on Learning Representations, available at: https://openreview.net/pdf?id=HyxY6JHKwr, last access: September 2019.
Dong, Q., Lin, Y., Bi, J., and Yuan, H.: An Integrated Deep Neural Network
Approach for Large-Scale Water Quality Time Series Prediction, in: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 6–9 October 2019, https://doi.org/10.1109/SMC.2019.8914404, IEEE, Bari, Italy, 3537–3542, 2019.
Ferguson, C. M., Croke, B. F., Beatson, P. J., Ashbolt, N. J., and Deere, D.
A.: Development of a process-based model to predict pathogen budgets for the
Sydney drinking water catchment, J. Water Health, 5, 187–208,
2007.
Fonseca, A., Botelho, C., Boaventura, R. A., and Vilar, V. J.: Integrated
hydrological and water quality model for river management: a case study on
Lena River, Sci. Total Environ., 485, 474–489, 2014.
Frolich, L., Vaizel-Ohayon, D., and Fishbain, B.: Prediction of Bacterial
Contamination Outbursts in Water Wells through Sparse Coding, Sci. Rep., 7, 1–11, https://doi.org/10.1038/s41598-017-00830-4, 2017.
Fujioka, R. S., Solo-Gabriele, H. M., Byappanahalli, M. N., and Kirs, M. US
recreational water quality criteria: a vision for the future, Int. J. Env. Res. Pub. He., 12,
7752–7776, https://doi.org/10.3390/ijerph120707752, 2015.
Gaillardet, J., Braud, I., Hankard, F., et al.: OZCAR: The French network of critical zone
observatories, Vadose Zone J., 17, 1–24,
https://doi.org/10.2136/vzj2018.04.0067, 2018.
Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G.: The soil and water
assessment tool: historical development, applications, and future research
directions, T. ASABE, 50, 1211–1250,
https://doi.org/10.13031/2013.23637, 2007.
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning: MIT press, Cambridge, Massachusetts, USA, 2016.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of automatic
calibration for hydrologic models: Comparison with multilevel expert
calibration, J. Hydrol. Eng., 4, 135–143, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135), 1999.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Heaphy, R. T., Burke, M. P., and Love, J. T.: Conversion of HSPF Legacy
Model to a Platform-Independent, Open-Source Language. AGUFM, 2015, H13C-1529, American Geophysical Union, Fall Meeting 2015, H13C-1529, December 2015.
Hinton, G. E., Osindero, S., and Teh, Y. W.: A fast learning algorithm for
deep belief nets, Neural Comput., 18, 1527–1554, https://doi.org/10.1162/neco.2006.18.7.1527, 2006.
Hochreiter, S. and Schmidhuber, J.: Long short-term memory, Neural Comput., 9, 1735–1780, 1997.
Iqbal, M. S., Islam, M. M., and Hofstra, N.: The impact of socio-economic
development and climate change on E. coli loads and concentrations in Kabul
River, Pakistan, Sci. Total Environ., 650, 1935–1943,
https://doi.org/10.1016/j.scitotenv.2018.09.347, 2019.
Isikdogan, F., Bovik, A. C., and Passalacqua, P.: Surface water mapping by
deep learning, IEEE J. Sel. Top. Appl., 10, 4909–4918, 2017.
Kawaguchi, K., Kaelbling, L. P., and Bengio, Y.: Generalization in deep
learning, arXiv [preprint], arXiv:1710.05468, 2017.
Kim, M., Boithias, L., Cho, K. H., Silvera, N., Thammahacksa, C.,
Latsachack, K., Rochelle-Newalld, E., Sengtaheuanghounge, O., Pierret, A., Pachepsky, Y. A., and Ribolzi, O.: Hydrological modeling of fecal
indicator bacteria in a tropical mountain catchment, Water Res., 119, 102–113, https://doi.org/10.1016/j.watres.2017.04.038, 2017.
Kim, M., Boithias, L., Cho, K. H., Sengtaheuanghoung, O., and Ribolzi, O.:
Modeling the Impact of Land Use Change on Basin-scale Transfer of Fecal
Indicator Bacteria: SWAT Model Performance, J. Environ. Qual., 47, 1115–1122, 2018.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Lee, D. H., Kim, J. H., Park, M.-H., Stenstrom, M. K., and Kang, J.-H.:
Automatic calibration and improvements on an instream chlorophyll a
simulation in the HSPF model, Ecol. Modell., 415, 108835, https://doi.org/10.1016/j.ecolmodel.2019.108835, 2020.
Lin, F., Chen, X., and Yao, H.: Evaluating the use of Nash-Sutcliffe
efficiency coefficient in goodness-of-fit measures for daily runoff
simulation with SWAT, J. Hydrol. Eng., 22, 05017023, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001580, 2017.
Lipton, Z. C.: The Mythos of Model Interpretability: In machine learning,
the concept of interpretability is both important and slippery, Queue, 16, 31–57,
2018.
Mazzocchi, F.: Could Big Data be the end of theory in science? A few remarks
on the epistemology of data-driven science, EMBO reports, 16, 1250–1255,
https://doi.org/10.15252/embr.201541001, 2015.
Mishra, A., Ahmadisharaf, E., Benham, B. L., Wolfe, M. L., Leman, S. C.,
Gallagher, D. L., Reckhow, K. H., and
Smith, E. P.: Generalized likelihood uncertainty
estimation and Markov chain Monte Carlo simulation to prioritize TMDL
pollutant allocations, J. Hydrol. Eng., 23, 05018025, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001720, 2018.
Mitchell, M.: Why AI is harder than we think, arXiv [preprint],
arXiv:2104.12871, 2021.
Molnar, C.: Interpretable machine learning, Lulu.com, available at: https://christophm.github.io/interpretable-ml-book/ (last access: 1 December 2021), 2020.
Molnar, C., Casalicchio, G., and Bischl, B.: Interpretable machine
learning – a brief history, state-of-the-art and challenges, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 417–431, 14–18 September 2020, Ghent, Belgium, https://doi.org/10.1007/978-3-030-65965-3_28, 2020.
Morris, M. D.: Factorial sampling plans for preliminary computational
experiments, Technometrics, 33, 161–174, 1991.
Meshesha, T. W., Wang, J., and Melaku, N. D.: A modified hydrological model
for assessing effect of pH on fate and transport of Escherichia coli in the
Athabasca River basin, J. Hydrol., 582, 124513, https://doi.org/10.1016/j.jhydrol.2019.124513, 2020.
Moriasi, D. N., Gitau, M. W., Pai, N., and Daggupati, P.: Hydrologic and
water quality models: Performance measures and evaluation criteria,
T. ASABE, 58, 1763–1785, 2015.
Muirhead, R. W. and Meenken, E. D.: Variability of Escherichia coli
Concentrations in Rivers during Base-Flow Conditions in New Zealand,
J. Environ. Qual., 47, 967–973, https://doi.org/10.2134/jeq2017.11.0458, 2018.
Nakhle, P., Ribolzi, O., Boithias, L., Rattanavong, S., Auda, Y., Sayavong,
S., Zimmermann, R., Soulileuth, B., Pando, A., and Thammahacksa, C.: Effects
of hydrological regime and land use on in-stream Escherichia coli
concentration in the Mekong basin, Lao PDR, Sci. Rep., 11, 1–17, 2021.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290, 1970.
Nash, S. G.: Newton-type minimization via the Lanczos method, SIAM J. Numer. Anal.,
21, 770–788, 1984.
Nguyen, H. T. M., Le, Q. T. P., Garnier, J., Janeau, J. L., and
Rochelle-Newall, E.: Seasonal variability of faecal indicator bacteria
numbers and die-off rates in the Red River basin, North Viet Nam,
Sci. Rep., 6, 1–12, https://doi.org/10.1038/srep21644, 2016.
Nair, V. and Hinton, G. E.: Rectified linear units improve restricted
boltzmann machines, in: ICML, Proceedings of the 27th International Conference on Machine Learning, 807–814, June, 2010.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
water assessment tool theoretical documentation version 2009, Texas Water
Resources Institute, Texas, USA, 2011.
Odonkor, S. T. and Ampofo, J. K.: Escherichia coli as an indicator of
bacteriological quality of water: an overview, Microbiology research, 4, e2-e2.
https://doi.org/10.4081/mr.2013.e2, 2013.
Palmateer, G., McLean, D., Kutas, W. L., and Meissner, S. M.: Suspended
particulate/bacterial interaction in agricultural drains, SS RAO, 1–40, CRC Press Inc., Florida, 1993.
Pachepsky, Y. and Shelton, D.: Escherichia coli and fecal coliforms in
freshwater and estuarine sediments, Crit. Rev. Env. Sci. Tec., 41, 1067–1110, 2011.
Pachepsky, Y. A., Blaustein, R. A., Whelan, G., and Shelton, D. R.:
Comparing temperature effects on Escherichia coli, Salmonella, and
Enterococcus survival in surface waters, Lett. Appl. Microbiol., 59, 278–283,
https://doi.org/10.1111/lam.12272, 2014.
Pachepsky, Y., Stocker, M., Saldaña, M. O., and Shelton, D.: Enrichment
of stream water with fecal indicator organisms during baseflow periods,
Environ. Monit. Assess., 189, 51, https://doi.org/10.1007/s10661-016-5763-8, 2017.
Pachepsky, Y. A., Allende, A., Boithias, L., Cho, K., Jamieson, R., Hofstra,
N., and Molina, M.: Microbial water quality: monitoring and modeling.
J. Environ. Qual., 47, 931–938, https://doi.org/10.2134/jeq2018.07.0277, 2018.
Pandey, P. K. and Soupir, M. L.: Assessing the impacts of E. coli laden
streambed sediment on E. coli loads over a range of flows and sediment
characteristics, J. Am. Water Resour. As., 49, 1261–1269, https://doi.org/10.1111/jawr.12079, 2013.
Park, Y., Kim, M., Pachepsky, Y., Choi, S. H., Cho, J. G., Jeon, J., and
Cho, K. H.: Development of a nowcasting system using machine learning
approaches to predict fecal contamination levels at recreational beaches in
Korea, J. Environ. Qual., 47, 1094–1102, https://doi.org/10.2134/jeq2017.11.0425, 2018.
Patin, J., Mouche, E., Ribolzi, O., Sengtahevanghoung, O., Latsachak, K.,
Soulileuth, B., Chaplot, V., and Valentin, C.: Effect of land use on
interrill erosion in a montane catchment of Northern Laos: An analysis based
on a pluri-annual runoff and soil loss database, J. Hydrol., 563,
480–494, 2018.
Peterson, K. T., Sagan, V., and Sloan, J. J.: Deep learning-based water
quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual
constellation and cloud computing, Gisci. Remote Sens., 57,
510–525, 2020.
Pool, S., Vis, M., and Seibert, J.: Evaluating model performance: towards a
non-parametric variant of the Kling-Gupta efficiency, Hydrol. Sci. J., 63, 1941–1953, 2018.
Pyo, J., Park, L. J., Pachepsky, Y., Baek, S. S., Kim, K., and Cho, K. H.:
Using convolutional neural network for predicting cyanobacteria
concentrations in river water, Water Res., 186, 116349,
https://doi.org/10.1016/j.watres.2020.116349, 2020.
Read, J. S., Jia, X., Willard, J., Appling, A. P., Zwart, J. A., Oliver, S.
K., Karpatne, A., Hansen, G. J. A., Hanson, P. C., Watkins, W., Steinbach, M., and Kumar, V.: Process-guided deep learning predictions of lake
water temperature, Water Resour. Res., 55, 9173–9190, https://doi.org/10.1029/2019WR024922, 2019.
Rochelle-Newall, E., Nguyen, T. M. H., Le, T. P. Q., Sengtaheuanghoung, O.,
and Ribolzi, O.: A short review of fecal indicator bacteria in tropical
aquatic ecosystems: knowledge gaps and future directions, Front. Microbiol., 6, 308,
https://doi.org/10.3389/fmicb.2015.00308, 2015.
Rochelle-Newall, E. J., Ribolzi, O., Viguier, M., Thammahacksa, C., Silvera,
N., Latsachack, K., Dinh, R. P., Naporn, P., Sy, H. T., and Soulileuth, B.:
Effect of land use and hydrological processes on Escherichia coli
concentrations in streams of tropical, humid headwater catchments,
Sci. Rep., 6, 1–12, 2016.
Ribolzi, O., Evrard, O., Huon, S., Rochelle-Newall, E., Henri-des-Tureaux,
T., Silvera, N., Thammahacksac, C., and Sengtaheuanghoung, O.: Use of fallout radionuclides
(7 Be, 210 Pb) to estimate resuspension of Escherichia coli from streambed
sediments during floods in a tropical montane catchment, Environ. Sci. Pollut. R., 23, 3427–3435,
2016.
Ribolzi, O., Evrard, O., Huon, S., De Rouw, A., Silvera, N., Latsachack, K.
O., Soulileuth, B., Lefèvre, I., Pierret, A., and Lacombe, G.: From
shifting cultivation to teak plantation: effect on overland flow and
sediment yield in a montane tropical catchment, Sci. Rep., 7, 1–12, 2017.
Ribolzi, O., Lacombe, G., Pierret, A., Robain, H., Sounyafong, P., De Rouw,
A., Soulileuth, B., Mouche, E., Huon, S., and Silvera, N.: Interacting land
use and soil surface dynamics control groundwater outflow in a montane
catchment of the lower Mekong basin, Agr. Ecosyst. Environ., 268, 90–102, 2018.
Ribolzi, O., Boithias, L., Thammahacksa, C., Rochelle-Newall, E., Pando‐Bahuon, A., Silvera, N., Sengtaheuanghoung, O., Sipaseuth, N., and Pierret, A.: Escherichia coli concentrations and physico-chemical measurements (2011–2021) at the outlet of the Houay Pano catchment, northern Lao PDR [Data set], https://doi.org/10.23708/EWOYNK, 2021.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J.: Learning
representations by back-propagating errors, Nature, 323, 533–536, https://doi.org/10.1038/323533a0, 1986.
Seong, C. H., Benham, B. L., Hall, K. M., and Kline, K.: Comparison of
alternative methods to simulate bacteria concentrations with HSPF under
low-flow conditions, Appl. Eng. Agric., 29, 917–931,
https://doi.org/10.13031/aea.29.10203, 2013.
Silvera, N., Ribolzi, O., Boithias, L., Rochelle-Newall, E., Riotte, J., Audry, S., Sipaseuth, N., Valentin, C., Janeau, J. L., Bricquet, J. P., Sengtaheuanghoung, O., Auda, Y., Chaplot, V., de Rouw, A., Henry-Des-Tureaux, T., Huon, S., Latsachack, K., Maeght, J. L., Pando, A., Pierret, A., Robain, H., Sayavong, S., Soulileuth, B., Souliyavongsa, X., Sounyafong, P., Thammahacksa, C., A., Viguier, M., Khampaseuth, X., Bourdon, E., Chanhphengxay, A., Le Troquer, Y., Lestrelin, G., Marchand, P., Moreau, P., Phachomphon, K., Phantahvong, K., Tasaketh, S., Thiebaux, J., Vigiak, O., and Noble, A.: 6 min rainfall data, Houay Pano, Laos [Data set], https://doi.org/10.6096/msec.laos.5, 2015a.
Silvera, N., Ribolzi, O., Boithias, L., Rochelle-Newall, E., Riotte, J., Audry, S., Sipaseuth, N., Valentin, C., Janeau, J. L., Bricquet, J. P., Sengtaheuanghoung, O., Auda, Y., Chaplot, V., de Rouw, A., Henry-Des-Tureaux, T., Huon, S., Latsachack, K., Maeght, J. L., Pando, A., Pierret, A., Robain, H., Sayavong, S., Soulileuth, B., Souliyavongsa, X., Sounyafong, P., Thammahacksa, C., A., Viguier, M., Khampaseuth, X., Bourdon, E., Chanhphengxay, A., Le Troquer, Y., Lestrelin, G., Marchand, P., Moreau, P., Phachomphon, K., Phantahvong, K., Tasaketh, S., Thiebaux, J., Vigiak, O., and Noble, A.: Hydrological data, Houay Pano, Laos [Data set], https://doi.org/10.6096/msec.laos.3, 2015b.
Silvera, N., Ribolzi, O., Boithias, L., Rochelle-Newall, E., Riotte, J., Audry, S., Sipaseuth, N., Valentin, C., Janeau, J. L., Bricquet, J. P., Sengtaheuanghoung, O., Auda, Y., Chaplot, V., de Rouw, A., Henry-Des-Tureaux, T., Huon, S., Latsachack, K., Maeght, J. L., Pando, A., Pierret, A., Robain, H., Sayavong, S., Soulileuth, B., Souliyavongsa, X., Sounyafong, P., Thammahacksa, C., A., Viguier, M., Khampaseuth, X., Bourdon, E., Chanhphengxay, A., Le Troquer, Y., Lestrelin, G., Marchand, P., Moreau, P., Phachomphon, K., Phantahvong, K., Tasaketh, S., Thiebaux, J., Vigiak, O., and Noble, A.: Land use data, Houay Pano, Laos [Data set], https://doi.org/10.6096/msec.laos.7, 2015c.
Silvera, N., Ribolzi, O., Boithias, L., Rochelle-Newall, E., Riotte, J., Audry, S., Sipaseuth, N., Valentin, C., Janeau, J. L., Bricquet, J. P., Sengtaheuanghoung, O., Auda, Y., Chaplot, V., de Rouw, A., Henry-Des-Tureaux, T., Huon, S., Latsachack, K., Maeght, J. L., Pando, A., Pierret, A., Robain, H., Sayavong, S., Soulileuth, B., Souliyavongsa, X., Sounyafong, P., Thammahacksa, C., A., Viguier, M., Khampaseuth, X., Bourdon, E., Chanhphengxay, A., Le Troquer, Y., Lestrelin, G., Marchand, P., Moreau, P., Phachomphon, K., Phantahvong, K., Tasaketh, S., Thiebaux, J., Vigiak, O., and Noble, A.: Weather station data, Houay Pano, Laos [Data set], https://doi.org/10.6096/msec.laos.6, 2015d.
Singh, A. and Kingsbury, N.: Dual-tree wavelet scattering network with
parametric log transformation for object classification, in: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2622–2626, IEEE, https://doi.org/10.1109/ICASSP.2017.7952631, March 2017.
Solanki, A., Agrawal, H., and Khare, K.: Predictive analysis of water
quality parameters using deep learning, Int. J. Comp. Appl., 125, 0975-8887, https://doi.org/10.5120/ijca2015905874, 2015.
Song, L., Boithias, L., Sengtaheuanghoung, O., Oeurng, C., Valentin, C.,
Souksavath, B., Sounyafong, P., de Rouw, A., Soulileuth, B., Silvera, N., Lattanavongkot, B., Pierret, A., and Ribolzi, O.: Understory Limits Surface Runoff and
Soil Loss in Teak Tree Plantations of Northern Lao PDR, Water, 12, 2327,
https://doi.org/10.3390/w12092327, 2020.
Sowah, R. A., Bradshaw, K., Snyder, B., Spidle, D., and Molina, M.:
Evaluation of the soil and water assessment tool (SWAT) for simulating E.
coli concentrations at the watershed-scale, Sci. Total Environ., 746, 140669,
https://doi.org/10.1016/j.scitotenv.2020.140669, 2020.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and
Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from
overfitting, J. Mach. Learn. Res., 15, 1929–1958, 2014.
Sze, V., Chen, Y. H., Yang, T. J., and Emer, J. S.: Efficient Processing of
Deep Neural Networks: A Tutorial and Survey,
https://doi.org/10.1109/JPROC.2017.2761740, Proceedings of the IEEE, 2295–2329, 2017.
Thupaki, P., Phanikumar, M. S., Schwab, D. J., Nevers, M. B., and Whitman,
R. L.: Evaluating the role of sediment-bacteria interactions on Escherichia
coli concentrations at beaches in southern Lake Michigan, J. Geophys. Res.-Oceans, 118, 7049–7065, https://doi.org/10.1002/2013JC008919, 2013.
Troeger, C., Forouzanfar, M., Rao, P. C., et al.: Estimates of global, regional, and national
morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic
analysis for the Global Burden of Disease Study 2015, Lancet. Infect. Dis., 17, 909–948, https://doi.org/10.1016/S1473-3099(17)30276-1, 2017.
Tiddi, I.: Directions for explainable knowledge-enabled systems, Knowledge
Graphs for eXplainable Artificial Intelligence: Foundations, Applications
and Challenges, 47, 245, ISBN 978-1-64368-080-4, 245–261, https://doi.org/10.3233/SSW200022, 2020.
Van Rossum, G.: Python programming language, in: USENIX annual technical conference, Vol. 41, p. 36, Santa Clara, CA, USA, June 2007.
Virtanen, P., Gommers, R., Oliphant, T. E., et al.: SciPy 1.0: fundamental
algorithms for scientific computing in Python, Nat. Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020
Wang, X., Zhang, F., and Ding, J.: Evaluation of water quality based on a
machine learning algorithm and water quality index for the Ebinur Lake
Watershed, China, Sci. Rep., 7, 1–18,
https://doi.org/10.1038/s41598-017-12853-y, 2017
Whitehead, P. G., Leckie, H., Rankinen, K., Butterfield, D., Futter, M., and
Bussi, G.: An INCA model for pathogens in rivers and catchments: Model
structure, sensitivity analysis and application to the River Thames
catchment, UK, Sci. Total Environ., 572, 1601–1610, 2016.
Xiang, Z., Yan, J., and Demir, I.: A rainfall-runoff model with LSTM-based
sequence-to-sequence learning, Water Resour. Res., 56, e2019WR025326.
https://doi.org/10.1029/2019WR025326, 2020.
Xiang, Z., Demir, I., Mantilla, R., and Krajewski Witold, F.: A Regional
Semi-Distributed Streamflow Model Using Deep Learning,
https://doi.org/10.31223/X5GW3V, 2011.
Van der Leeuw, S. E.: Why model?, Cybernet. Syst., 35, 117–128,
https://doi.org/10.1080/01969720490426803, 2004.
Yagow, G., Dillaha, T., Mostaghimi, S., Brannan, K., Heatwole, C., and
Wolfe, M. L.: TMDL modeling of fecal coliform bacteria with HSPF, in: 2001 ASAE Annual Meeting, p. 1, American Society of Agricultural and Biological Engineers, 2950 Niles Road, St. Joseph, MI 49085, Copyright © 2021 American Society of Agricultural and Biological Engineers, 1998.
Zheng, A. and Casari, A.: Feature engineering for machine learning: principles and techniques for data scientists, O'Reilly Media, Inc., 1005 Gravenstein Highway North Sebastopol, CA 95472, USA, 2018.
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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....
