Performance Evaluation of Pipe Break Machine Learning Models Using Datasets from Multiple Utilities-Reference-Cited by-同舟云学术

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Performance Evaluation of Pipe Break Machine Learning Models Using Datasets from Multiple Utilities

Published:2022-06 Issue:2 Volume:28 Page:
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Chen Thomas Ying-Jeh¹,Vladeanu Greta²,Yazdekhasti Sepideh³,Daly Craig Michael⁴

Affiliation:

1. Research Data Scientist, Xylem Inc., 8920 MD-108, Columbia, MD 21045 (corresponding author).

2. Research Analyst, Xylem Inc., 8920 MD-108, Columbia, MD 21045.

3. Decision Science Manager, Xylem Inc., 8920 MD-108, Columbia, MD 21045.

4. Chief Engineer, Xylem Inc., 8920 MD-108, Columbia, MD 21045.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IS.1943-555X.0000683

Reference40 articles.

1. Forecasting watermain failure using artificial neural network modelling

2. A game plan for aging water infrastructure

3. Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks

4. Chen T. and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining 785–794. New York: Association for Computing Machinery.

5. Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks

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