EPXWGNXE
type: UserItem
resource: Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort
access date: 2022-04-14 09:33:40
repository: www.mdpi.com
rights: http://creativecommons.org/licenses/by/3.0/
tagged: AutoTag
  label: Escherichia coli concentration
tagged: AutoTag
  label: machine learning
tagged: AutoTag
  label: optimized sampling
tagged: AutoTag
  label: river monitoring
tagged: AutoTag
  label: water quality prediction

Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort
type: AcademicArticle
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.
pages: 2457
doi: 10.3390/w13182457
language: en
extra: Number: 18
creator: Person
author list: Person
is part of: Issue
  date: 2021/1
  volume: 13
  issue: 18
  is part of: Water
    type: Journal
    publisher: Multidisciplinary Digital Publishing Institute
    issn: 2073-4441

Person
given name: Manel
surname: Naloufi

Person
given name: Françoise S.
surname: Lucas

Person
given name: Sami
surname: Souihi

Person
given name: Pierre
surname: Servais

Person
given name: Aurélie
surname: Janne

Person
given name: Thiago
surname: Wanderley Matos De Abreu