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Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations, Sanitary Engineering, Water Resources

Norovirus infections are among the major causes of acute gastroenteritis worldwide. In Germany, norovirus infections are the most frequently reported cause of gastroenteritis, although only laboratory confirmed cases are officially counted. The high infectivity and environmental persistence of norovirus, makes the virus a relevant pathogen for water related infections. In the 2017 guidelines for potable water reuse, the World Health Organization proposes Norovirus as a reference pathogen for viral pathogens for quantitative microbial risk assessment (QMRA). A challenge for QMRA is, that norovirus data are rarely available over long monitoring periods to assess inter-annual variability of the associated health risk, raising the question about the relevance of this source of variability regarding potential risk management alternatives. Moreover, norovirus infections show high prevalence during winter and early spring and lower incidence during summer. Therefore, our objective is to derive risk scenarios for assessing the potential relevance of the within and between year variability of norovirus concentrations in municipal wastewater for the assessment of health risks of fieldworkers, if treated wastewater is used for irrigation in agriculture. To this end, we use the correlation between norovirus influent concentration and reported epidemiological incidence (R²=0.93), found at a large city in Germany. Risk scenarios are subsequently derived from long-term reported epidemiological data, by applying a Bayesian regression approach. For assessing the practical relevance for wastewater reuse we apply the risk scenarios to different irrigation patterns under various treatment options, namely “status-quo” and “irrigation on demand”. While status-quo refers to an almost all-year irrigation, the latter assumes that irrigation only takes place during the vegetation period from May - September. Our results indicate that the log-difference of infection risks between scenarios, Sanitary Engineering, Water Resources

Objective: this paper focused on: (i) developing a deterioration model based on support vector machines (SVM) from its regression approach to separate the prediction of the structural condition of sewer pipes from a classification by grades and predict the scores obtained by failures found in CCTV inspections; and (ii) comparing the prediction results of the proposed model with the ones obtained by a deterioration model based on SVM classification tasks to explore the advantages and disadvantages of their predictions from different perspectives. Materials and methods: The sewer network of Bogota was the case study for this work in which a dataset consisting of the characteristics of 5031 pipes inspected by CCTV (obtained by GIS) was considered, as well as information on external variables (e.g., age, sewerage, and road type). Probability density functions (PDF) were used to convert the scores given by failures found in CCTV into structural grades. In addition, three techniques were used to evaluate the predictions from different perspectives: positive likelihood rate (PLR), performance curve and deviation analysis. Results: it was found that: (i) SVM-based deterioration model used from its regression approach is suitable to predict critical structural conditions of uninspected sewer pipes because this model showed a PLR value around 6.8 (the highest value among the predictions of all structural conditions for both models) and 74 % of successful predictions for the first 100 pipes with the highest probability of being in critical conditions; and (ii) SVM-based deterioration model used from its classification approach is suitable to predict other structural conditions because this model showed homogeneous PLR values for the prediction of all structural conditions (PLR values between 1.67 and 3.88) and deviation analysis results for all structural conditions are lower than the ones for the SVM-based model from its regression approach, Objetivo: este artículo se centra en: (i) desarrollar un modelo de deterioro basado en máquinas de soporte vectorial (SVM) a partir de su enfoque regresivo para desligar la predicción de la condición estructural del alcantarillado de los grados de clasificación y predecir los puntajes dados por las fallas encontradas en las inspecciones CCTV; y (ii) comparar los resultados de predicción del modelo propuesto con aquellos resultados obtenidos de un modelo basado en SVM a partir de su enfoque de clasificación con el fin de explorar las ventajas y desventajas en sus predicciones bajo diferentes perspectivas. Materiales y métodos: el caso de estudio considerado fue la red de alcantarillado de Bogotá, el cual contaba con 5031 tuberías inspeccionadas, información de las características físicas de las tuberías e información de factores externos (p. e., edad, tipo de afluente y tipo de vía). Las funciones de densidad de probabilidad (FDP) se utilizaron para convertir los puntajes de las fallas encontradas en las inspecciones de CCTV en grados estructurales. Además, se utilizaron tres técnicas para evaluar las predicciones mediante diferentes perspectivas: tasas de verosimilitud positiva (TPR), curvas de rendimiento y análisis de desviación. Resultados: se encuentra que: (i) el modelo de deterioro basado en SVM a partir de su enfoque de regresión es apropiado para predecir condiciones estructurales críticas, ya que este modelo muestra valores de TPR alrededor de 6.8 (el valor más alto entre la predicción de todas las condiciones estructurales en ambos modelos) y 74 % de predicciones exitosas en las primeras 100 tuberías con más altas probabilidades de estar en condición crítica; y (ii) el modelo de deterioro basado en SVM a partir de su enfoque de clasificación es apropiado para predecir las otras condiciones estructurales, ya que este modelo muestra valores de PLR homogéneos para la predicción de todas las condiciones estructurales (entre 1.67 y 3.88) y las desviaciones entr

Objective: this paper focused on: (i) developing a deterioration model based on support vector machines (SVM) from its regression approach to separate the prediction of the structural condition of sewer pipes from a classification by grades and predict the scores obtained by failures found in CCTV inspections; and (ii) comparing the prediction results of the proposed model with the ones obtained by a deterioration model based on SVM classification tasks to explore the advantages and disadvantages of their predictions from different perspectives. Materials and methods: The sewer network of Bogota was the case study for this work in which a dataset consisting of the characteristics of 5031 pipes inspected by CCTV (obtained by GIS) was considered, as well as information on external variables (e.g., age, sewerage, and road type). Probability density functions (PDF) were used to convert the scores given by failures found in CCTV into structural grades. In addition, three techniques were used to evaluate the predictions from different perspectives: positive likelihood rate (PLR), performance curve and deviation analysis. Results: it was found that: (i) SVM-based deterioration model used from its regression approach is suitable to predict critical structural conditions of uninspected sewer pipes because this model showed a PLR value around 6.8 (the highest value among the predictions of all structural conditions for both models) and 74 % of successful predictions for the first 100 pipes with the highest probability of being in critical conditions; and (ii) SVM-based deterioration model used from its classification approach is suitable to predict other structural conditions because this model showed homogeneous PLR values for the prediction of all structural conditions (PLR values between 1.67 and 3.88) and deviation analysis results for all structural conditions are lower than the ones for the SVM-based model from its regression approach, Objetivo: este artículo se centra en: (i) desarrollar un modelo de deterioro basado en máquinas de soporte vectorial (SVM) a partir de su enfoque regresivo para desligar la predicción de la condición estructural del alcantarillado de los grados de clasificación y predecir los puntajes dados por las fallas encontradas en las inspecciones CCTV; y (ii) comparar los resultados de predicción del modelo propuesto con aquellos resultados obtenidos de un modelo basado en SVM a partir de su enfoque de clasificación con el fin de explorar las ventajas y desventajas en sus predicciones bajo diferentes perspectivas. Materiales y métodos: el caso de estudio considerado fue la red de alcantarillado de Bogotá, el cual contaba con 5031 tuberías inspeccionadas, información de las características físicas de las tuberías e información de factores externos (p. e., edad, tipo de afluente y tipo de vía). Las funciones de densidad de probabilidad (FDP) se utilizaron para convertir los puntajes de las fallas encontradas en las inspecciones de CCTV en grados estructurales. Además, se utilizaron tres técnicas para evaluar las predicciones mediante diferentes perspectivas: tasas de verosimilitud positiva (TPR), curvas de rendimiento y análisis de desviación. Resultados: se encuentra que: (i) el modelo de deterioro basado en SVM a partir de su enfoque de regresión es apropiado para predecir condiciones estructurales críticas, ya que este modelo muestra valores de TPR alrededor de 6.8 (el valor más alto entre la predicción de todas las condiciones estructurales en ambos modelos) y 74 % de predicciones exitosas en las primeras 100 tuberías con más altas probabilidades de estar en condición crítica; y (ii) el modelo de deterioro basado en SVM a partir de su enfoque de clasificación es apropiado para predecir las otras condiciones estructurales, ya que este modelo muestra valores de PLR homogéneos para la predicción de todas las condiciones estructurales (entre 1.67 y 3.88) y las desviaciones entr