Prediction of pipe failures in water supply networks for longer time periods through multi-label classification-Reference-Cited by-同舟云学术

Prediction of pipe failures in water supply networks for longer time periods through multi-label classification

Published:2023-03 Issue: Volume:213 Page:119050
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Robles-Velasco Alicia,Cortés Pablo,Muñuzuri Jesús,De Baets Bernard

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference71 articles.

1. Risk-based prioritization of water main failure using fuzzy synthetic evaluation technique;Al-Zahrani;Journal of Water Supply: Research and Technology - AQUA,2016

2. Intelligent approaches for predicting failure of water mains;Almheiri;Journal of Pipeline Systems Engineering and Practice,2020

3. Amaitik, N. M., & Buckingham, C. D. (2017). Developing a hierarchical fuzzy rule-based model with weighted linguistic rules: A case study of water pipes condition prediction. In Computing Conference (pp. 30–40). London, UK. https://doi.org/10.1109/SAI.2017.8252078.

4. Estimation of failure rate in water distribution network using fuzzy clustering and LS-SVM methods;Aydogdu;Water Resources Management,2015

5. The challenges of predicting pipe failures in clean water networks: A view from current practice;Barton;Water Supply,2022

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