Water Demand Forecasting with Multi-Objective Computational Intelligence-Reference-Cited by-同舟云学术

Water Demand Forecasting with Multi-Objective Computational Intelligence

Published:2024-09-06 Issue: Volume:749 Page:79
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
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Author:

Reynoso-Meza Gilberto¹²^ORCID,Carreño-Alvarado Elizabeth Pauline²³

Affiliation:

1. Programa de Pós-Graduação em Engenharia de Produção e Sistemas (PPGEPS), Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba 80215-901, Brazil

2. Laboratório de Otimização de Sistemas (LOSC), Pontifícia Universidade Católica do Paraná (PUCPR), Curitiba 80215-901, Brazil

3. Programa de Pós-graduação em Engenharia de Recursos Hídricos e Ambiental (PPGERHA), Universidade Federal do Paraná (UFPR), Curitiba 82590-300, Brazil

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/79/pdf

Reference4 articles.

1. A novel dynamic multi-criteria ensemble selection mechanism applied to drinking water quality anomaly detection;Ribeiro;Sci. Total Environ.,2020

2. Multi-objective Logistic Regression for Anomaly Detection in Water Distribution Systems;Rocha;Communication, Smart Technologies and Innovation for Society: Proceedings of CITIS 2021,2022

3. Carreño-Alvarado, E.P., Hernández Alba, M., and Reynoso-Meza, G. (2022, January 18–22). Multi-objective insights and analysis on data driven classifiers for anomaly detection in water distribution systems. Proceedings of the 2nd International Joint Conference on Water Distribution Systems Analysis & Computing and Control in the Water Industry, Valencia, Spain.

4. Multi-criteria Decision-Making Techniques for the Selection of Pareto-optimal Machine Learning Models in a Drinking-Water Quality Monitoring Problem;Int. J. Inf. Technol. Decis. Mak.,2024