Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort Article Swipe
Manel Naloufi
,
Françoise S. Lucas
,
Sami Souihi
,
Pierre Servais
,
Aurélie Janne
,
Thiago Abreu
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/w13182457
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/w13182457
Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.
Related Topics
Concepts
Random forest
Water quality
Sampling (signal processing)
Environmental science
Decision tree
Extreme learning machine
Support vector machine
Computer science
Machine learning
Artificial neural network
Statistics
Hydrology (agriculture)
Mathematics
Ecology
Engineering
Biology
Geotechnical engineering
Computer vision
Filter (signal processing)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w13182457
- https://www.mdpi.com/2073-4441/13/18/2457/pdf
- OA Status
- gold
- Cited By
- 26
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198104958
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3198104958Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/w13182457Digital Object Identifier
- Title
-
Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling EffortWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-07Full publication date if available
- Authors
-
Manel Naloufi, Françoise S. Lucas, Sami Souihi, Pierre Servais, Aurélie Janne, Thiago AbreuList of authors in order
- Landing page
-
https://doi.org/10.3390/w13182457Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4441/13/18/2457/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/2073-4441/13/18/2457/pdfDirect OA link when available
- Concepts
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Random forest, Water quality, Sampling (signal processing), Environmental science, Decision tree, Extreme learning machine, Support vector machine, Computer science, Machine learning, Artificial neural network, Statistics, Hydrology (agriculture), Mathematics, Ecology, Engineering, Biology, Geotechnical engineering, Computer vision, Filter (signal processing)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 9, 2023: 3, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.recreational | 6 |
| abstract_inverted_index.stakeholders | 38 |
| abstract_inverted_index.successfully | 98 |
| abstract_inverted_index.(temperature, | 178 |
| abstract_inverted_index.conductivity, | 179 |
| abstract_inverted_index.optimization. | 258 |
| abstract_inverted_index.concentrations | 106 |
| abstract_inverted_index.microbiological | 53, 223 |
| abstract_inverted_index.time-consuming, | 31 |
| abstract_inverted_index.gastrointestinal | 11 |
| abstract_inverted_index.labor-intensive, | 32 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5061318268, https://openalex.org/A5051446810 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I142631665, https://openalex.org/I197681013, https://openalex.org/I2800365227, https://openalex.org/I4210126119, https://openalex.org/I4210148169 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.8178199 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |