Data-driven prediction of daily Cryptosporidium river concentrations for water resource management: Use of catchment-averaged vs spatially distributed features in a Bagging-XGBoost model

Cryptosporidium is a waterborne pathogen which poses a major challenge to water utilities because of its resistance to chlorination and its infectivity at very low concentrations. The ability to make predictions of Cryptosporidium concentrations in rivers would aid significantly in abstraction-based...

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Bibliographic Details
Published in:	The Science of the total environment Vol. 991; p. 179794
Main Authors:	Smalley, Alan L., Douterelo, Isabel, Chipps, Michael, Shucksmith, James D.
Format:	Journal Article
Language:	English
Published:	Netherlands Elsevier B.V 20.08.2025
Subjects:	Abstraction management Boosting Machine Learning Algorithms Catchment modelling Conservation of Water Resources - methods Cryptosporidium Cryptosporidium - isolation & purification England Environmental Monitoring - methods Machine learning Models, Theoretical Public health Rivers - parasitology Surface water Water quality Water Resources Water Supply England Cryptosporidium Abstraction management Catchment modelling Water quality Machine learning Surface water Public health
ISSN:	0048-9697, 1879-1026, 1879-1026
Online Access:	Get full text
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