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

Abstract

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.

Know Before You Go: Data-Driven Beach Water Quality Forecasting

“…We trained four models for each forecast lead time using the following statistical and machine learning model types: binary logistic regression, support vector machine, random forest, and gradient boosted machine. Each model type is available in the scikit-learn package in Python and has been used previously to predict FIB or other water quality parameters. − Binary logistic regression (BLR) is a statistical model that has an interpretable relationship between environmental variables and predicted FIB. We used a BLR model with an “elastic net” penalty.…”

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

“…We found diverse applications of the GeoAI methods in WQ spatio-temporal pattern analysis, the classification of WQ, and the prediction of WQ variables and the pollutant loading estimation. A detailed review of the ML application in WQ prediction is found in Rajaee et al [27], Naloufi et al [29], and Chen et al [194]. Table 4 shows examplesof GeoAI applications for this purpose.…”

“…Existing reviews of GeoAI and ML applications in hydrological modeling and fluvial studies have covered specific topics, such as the prediction of runoff, floods, and water quality [25][26][27][28][29]. Other reviews have focused on applying a particular GeoAI and ML method [30][31][32][33].…”

Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA

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