Impact of Data Preparation on the Performance of Pipe Break Status Prediction Models
Author:
Affiliation:
1. Assistant Professor, Dept. of Building, Civil, and Environmental Engineering, Gina Cody School of Engineering and Computer Science, Concordia Univ., Montreal, QC, Canada.
Publisher
American Society of Civil Engineers
Link
https://ascelibrary.org/doi/pdf/10.1061/9780784484852.085
Reference23 articles.
1. Data Mining Algorithms for Water Main Condition Prediction—Comparative Analysis;Assad A.;Journal of Water Resources Planning and Management,2022
2. Comprehensive review of structural deterioration of water mains: physically based models
3. Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks
4. Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks
5. Performance Evaluation of Pipe Break Machine Learning Models Using Datasets from Multiple Utilities;Chen T. Y. J.;Journal of Infrastructure Systems,2022
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1. Developing an Open Repository of Water Main Break Prediction Models in Kitchener;The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024);2024-08-29
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