Publication detail

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

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

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

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

RIV year

2014

Released

30. 4. 2014

Publisher

Elsevier

Location

Philadelphia, USA

ISBN

1877-7058

Periodical

Procedia Engineering

Year of study

70

Number

1

State

Kingdom of the Netherlands

Pages from

399

Pages to

408

Pages count

10

URL

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

Full text in the Digital Library

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"
}