Detail publikace

Using artificial neural network models to assess water quality in water distribution networks

CUESTA CORDOBA, G. TUHOVČÁK, L. TAUŠ, M.

Originální název

Using artificial neural network models to assess water quality in water distribution networks

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The purpose of the research is to assess chlorine concentration in WDS using statistical models based on ANN in combination with Monte-Carlo. This approach offers advantages in contrast to the generally use methods for modeling of chlorine decay in drinking water systems until now. The model was tested on one specific location using the hydraulic and water quality parameters such as flow, pH, temperature, etc. The model allows forecasting chlorine concentration at selected nodes of the water supply system. The results obtained in these selected nodes allow then to compare the chlorine concentration with EPANET in the system under assessment.

Klíčová slova

water distribution systems, chlorine decay, artificial neural networks, Monte Carlo Method

Autoři

CUESTA CORDOBA, G.; TUHOVČÁK, L.; TAUŠ, M.

Rok RIV

2014

Vydáno

30. 4. 2014

Nakladatel

Elsevier

Místo

Philadelphia, USA

ISSN

1877-7058

Periodikum

Procedia Engineering

Ročník

70

Číslo

1

Stát

Nizozemsko

Strany od

399

Strany do

408

Strany počet

10

URL

https://www.sciencedirect.com/science/article/pii/S1877705814000472

Plný text v Digitální knihovně

http://hdl.handle.net/11012/194720

BibTex

@inproceedings{BUT108425,
  author="Gustavo Andres {Cuesta Cordoba} and Ladislav {Tuhovčák} and Miloslav {Tauš}",
  title="Using artificial neural network models to assess water quality in water distribution networks",
  booktitle="PROCEDIA ENGINEERING, volume 70",
  year="2014",
  journal="Procedia Engineering",
  volume="70",
  number="1",
  pages="399--408",
  publisher="Elsevier",
  address="Philadelphia, USA",
  doi="10.1016/j.proeng.2014.02.045",
  issn="1877-7058",
  url="https://www.sciencedirect.com/science/article/pii/S1877705814000472"
}