Your browser doesn't support javascript.
loading
Using data mining techniques to isolate chemical intrusion in water distribution systems.
Barros, Daniel Bezerra; Cardoso, Sandra Maria; Oliveira, Eva; Brentan, Bruno; Ribeiro, Lubienska.
Afiliación
  • Barros DB; Hydraulic Engineering and Water Resources Department - School of Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil. danielbezerrab@gmail.com.
  • Cardoso SM; School of Technology, University of Campinas, Campinas, Brazil.
  • Oliveira E; School of Technology, University of Campinas, Campinas, Brazil.
  • Brentan B; Hydraulic Engineering and Water Resources Department - School of Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil.
  • Ribeiro L; School of Technology, University of Campinas, Campinas, Brazil.
Environ Monit Assess ; 194(3): 203, 2022 Feb 19.
Article en En | MEDLINE | ID: mdl-35182211
The security of water distribution systems has become the subject of an increasing volume of research over the last decade. Data analysis and machine learning are linked to hydraulic and quality modeling for improving the capacity of water utilities to save lives when faced with the contamination of water networks. This research applies k-nearest neighbor and random forest algorithms to estimate the location of contamination sources at near-real time. Epanet and Epanet-MSX software are used to simulate intrusions of pesticide into water distribution system and the interaction with compounds already present in water bulk. Different pesticide concentrations are considered in the simulations, and chlorine monitoring occurs through placed quality sensors. The results show that random forest can localize [Formula: see text] of contamination scenarios, while the KNN algorithm found [Formula: see text]. Finally, an assessment of contamination spread is made for a better understanding of the impacts of non-localized contamination.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Abastecimiento de Agua / Agua Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2022 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Abastecimiento de Agua / Agua Idioma: En Revista: Environ Monit Assess Asunto de la revista: SAUDE AMBIENTAL Año: 2022 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Países Bajos