Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

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

Evaluating the performance of machine learning approaches to predict the microbial quality of surface waters and to optimize the sampling effort

Exposure to contaminated water during aquatic recreational activities can lead to gastroin-testinal 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, conductiv-ity, 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.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Modélisation hydrodynamique 3D pour l’évaluation de la qualité de l’eau en milieu urbain – application au Bassin de La Villette (Paris, France)

International audienceThe spatiotemporal distribution of contaminants in watercourses is an important aspect to consider, particularly to anticipate decision-making for sanitary risk management. Discharge variation and thermal stratification impact this spatiotemporal distribution between an upstream monitoring point and a downstream area of interest. In our study site, Bassin de La Villette (Paris, France), a three-dimensional hydrodynamic model (TELEMAC-3D) was used to estimate the impact of hydro-meteorological conditions on the transport of a microbiological contamination from upstream to downstream, where a bathing area is open during Summer. The model was validated by comparing simulation results with high-frequency field data of water temperature and electrical conductivity. Two different periods of hot weather, and two with large conductivity variations were simulated. The modelling results of water temperature and conductivity were in good agreement with field data. Finally, a period of bacterial contamination following a rain episode was simulated to illustrate the model capability to reproduce the contamination transport.La distribution spatio-temporelle des contaminants dans les cours d'eau doit être prise en compte, notamment pour les prises de décision visant à réduire les risques sanitaires. Les variations de débit et la stratification thermique ont un impact sur cette distribution spatio-temporelle, entre un point de surveillance en amont et une zone d'intérêt en aval. Dans notre site d'étude, le Bassin de La Villette (Paris, France), un modèle hydrodynamique tridimensionnel (TELEMAC-3D) a été utilisé pour estimer l'impact des conditions hydro-météorologiques sur le transport d’une contamination microbiologique de l'amont vers l'aval, où une zone de baignade est ouverte en été. Le modèle a été validé en comparant les résultats de simulation de la température de l'eau et de la conductivité électrique avec des données de terrain à haute fréquence. Deux périodes estivales chaudes, et deux périodes montrant de grandes variations de conductivité ont été simulées. Les résultats de modélisation de la température de l'eau et de la conductivité ont montré un bon accord avec les données de terrain. Enfin, une période de contamination bactérienne survenant après un épisode de pluie a été simulée pour illustrer la capacité du modèle à reproduire le transport de la contamination

Fluorescence spectroscopy for tracking microbiological contamination in urban waterbodies

International audienceDissolved organic matter (DOM) plays a crucial role in freshwater ecosystem function. Monitoring of DOM in aquatic environments can be achieved by using fluorescence spectroscopy. Particularly, DOM fluorescence can constitute a signature of microbiological contamination with a potential for high frequency monitoring. However, limited data are available regarding urban waterbodies. This study considers fluorescence data from field campaigns conducted in the Paris metropolitan region: two watercourses (La Villette basin and the river Marne), two stormwater network outlets (SO), and a wastewater treatment plant effluent (WWTP-O). The objectives of the study were to characterize the major fluorescence components in the studied sites, to investigate the impact of local rainfall in such components and to identify a potential fluorescence signature of local microbiological contamination. The components of a PARAFAC model (C1-C7), corresponding to a couple of excitation (ex) and emission (em) wavelengths, and the fluorescence indices HIX and BIX were used for DOM characterization. In parallel, fecal indicator bacteria (FIB) were measured in selected samples. The PARAFAC protein-like components, C6 (ex/em of 280/352 nm) and C7 (ex/em of 305/340 nm), were identified as markers of microbial contamination in the studied sites. In the La Villette basin, where samplings covered a period of more than 2 years, which also included similar numbers of wet and dry weather samples, the protein-like components were significantly higher in wet weather in comparison to dry weather. A positive relationship was obtained between C6 and FIB. In urban rivers, the high frequency monitoring of C6 levels would support the fecal contamination detection in rivers. In addition, it could help targeting specific field campaigns to collect comprehensive dataset of microbiological contamination episodes

Spectroscopie de fluorescence de la matière organique dissoute pour la surveillance de la qualité de l'eau dans les plans d'eau urbains

Dissolved organic matter (DOM) plays a key role in the freshwater ecosystem function. Fluorescencespectroscopy can be used to monitor DOM in aquatic environments. In urban waterbodies, wastewater dischargeand runoff during rainfall events affect the water quality and change the DOM composition. Few data areavailable in urban waters. In this paper, we present fluorescence data obtained through field campaignsconducted during dry and wet weather, in watercourses in the Paris metropolitan region (France). Samples werecollected in two water bodies (Marne River and La Villette canal), two stormwater outlet and the outlet of awastewater treatment plant. Fluorescence indices, used in the literature to estimate the DOM composition, werecalculated. Based on these indices, assumptions on the DOM composition of the samples in wet weather weredone. After further validation, they could be used as indicators of microbiological contamination in the studiedwatercourses.La matière organique dissoute (MOD) joue un rôle essentiel dans le fonctionnement des milieux aquatiques. Laspectroscopie de fluorescence peut être utilisée pour la surveillance de la MOD en milieu aquatique. Dans lesmasses d'eau urbaines, les rejets d'eaux usées et le ruissellement pendant les événements pluvieux affectent laqualité de l'eau et modifient la composition de la MOD. Peu de données sont disponibles dans les eaux urbaines.Dans cet article, des mesures de fluorescence sur des échantillons collectés par temps sec et temps de pluie,dans des cours d’eau de la région métropolitaine de Paris (France), sont présentées. L’échantillonnage a étéréalisé dans deux cours d'eau (rivière Marne et canal de la Villette), deux exutoires de réseau de drainage pluvialet le rejet d'une station d'épuration des eaux usées. Des index de fluorescence, utilisés dans la littérature pourestimer l’origine de la MOD, ont été calculés. Basés sur les données de fluorescence, ils ont permis d’émettre deshypothèses sur l’évolution de la MOD par temps de pluie et pourront servir d’indicateurs de contaminationmicrobiologique dans les cours d’eau étudiés

Long-Term Stability of Low-Cost IoT System for Monitoring Water Quality in Urban Rivers

Monitoring water quality in urban rivers is crucial for water resource management since point and non-point source pollution remain a major challenge. However, traditional water quality monitoring methods are costly and limited in frequency and spatial coverage. To optimize the monitoring, techniques such as modeling have been proposed. These methods rely on networks of low-cost multiprobes integrated with IoT networks to offer continuous real-time monitoring, with sufficient spatial coverage. But challenges persist in terms of data quality. Here, we propose a framework to verify the reliability and stability of low-cost sensors, focusing on the implementation of multiparameter probes embedding six sensors. Various tests have been developed to validate these sensors. First of all, a calibration check was carried out, indicating good accuracy. We then analyzed the influence of temperature. This revealed that for the conductivity and the oxygen sensors, a temperature compensation was required, and correction coefficients were identified. Temporal stability was verified in the laboratory and in the field (from 3 h to 3 months), which helped identify the frequency of maintenance procedures. To compensate for the sensor drift, weekly calibration and cleaning were required. This paper also explores the feasibility of LoRa technology for real-time data retrieval. However, with the LoRa gateways tested, the communication distance with the sensing device did not exceed 200 m. Based on these results, we propose a validation method to verify and to assure the performance of the low-cost sensors for water quality monitoring