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

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 <i>Escherichia coli</i> is routinely monitored, which is time-consuming, labor-intensive, and costly. To assis...

Full description

Bibliographic Details
Main Authors: Manel Naloufi, Françoise S. Lucas, Sami Souihi, Pierre Servais, Aurélie Janne, Thiago Wanderley Matos De Abreu
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Water
Subjects:
water quality prediction
machine learning
<i>Escherichia coli</i> concentration
optimized sampling
river monitoring
Online Access:https://www.mdpi.com/2073-4441/13/18/2457

Internet

https://www.mdpi.com/2073-4441/13/18/2457

Similar Items