2009 journal article

Contaminant Source Identification in Water Distribution Networks Under Conditions of Demand Uncertainty


By: P. Vankayala n, A. Sankarasubramanian n, S. Ranjithan n & G. Mahinthakumar n

author keywords: contaminant source identification; water distribution system; noisy genetic algorithms; uncertainty; optimization simulation
TL;DR: Results show that noisy GA is more robust and is less computationally expensive than stochastic GA in solving the source identification problem and the autoregressive demand uncertainty model better represents the uncertainty in the source Identification process than the Gaussian model. (via Semantic Scholar)
UN Sustainable Development Goal Categories
6. Clean Water and Sanitation (Web of Science; OpenAlex)
Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.