A Methodology for Forecasting Demands in a Water Distribution Network Based on the Classical and Neural Networks Approach-Reference-Cited by-同舟云学术

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A Methodology for Forecasting Demands in a Water Distribution Network Based on the Classical and Neural Networks Approach

Published:2024-09-02 Issue: Volume:189 Page:29
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
language:
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Author:

Coy Yesid¹^ORCID,González Laura¹^ORCID,Basto Laura¹^ORCID,Rodríguez Valeria¹^ORCID,Gómez Santiago¹^ORCID,Perafán Juan¹,Cardona Simón¹,Tabares Alejandra²^ORCID,Saldarriaga Juan³^ORCID

Affiliation:

1. Water Distribution and Sewerage Systems Research Center, Universidad de los Andes, Bogotá 111711, Colombia

2. Industrial Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia

3. Civil and Environmental Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/29/pdf

Reference7 articles.

1. Water Demand Forecasting: Review of Soft Computing Methods;Ghalehkhondabi;Environ. Monit. Assess.,2017

2. Water Consumption Patterns as a Basis for Water Demand Modeling;Avni;Water Resour. Res.,2015

3. Xiao, Y., Kong, W., and Liang, Z. (2022). Short-Term Demand Forecasting of Urban Online Car-Hailing Based on the K-Nearest Neighbor Model. Sensors, 22.

4. Kühnert, C., Gonuguntla, N.M., Krieg, H., Nowak, D., and Thomas, J.A. (2021). Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control. Water, 13.

5. Long Short-Term Memory;Hochreiter;Neural Comput.,1997

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