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A platform for research: civil engineering, architecture and urbanism
A Comparison Study of Water Pipe Failure Prediction Models
Water leakages have been a major problem for water supply companies, one of the main causes of this problem is pipe failure in the water supply network. The risk prediction models of the pipe failure are also constantly being improved to determine the location of these leaks accurately and quickly. The statistical model is the Logistic Regression model (LR) used for failure prediction in groups of pipes. Machine Learning approaches, particularly the Decision Tree model (DT) and the Artificial Neural Network model (ANN) are compared in predicting individual pipe failure. In this paper, we proposed applying these three models to predict pipe failure for the DMA17 water supply network in ward 17, Go Vap District, Ho Minh city. Using Area Under the Curve (AUC) value to evaluate the modeling results, comparing this value showed that the ANN was the most suitable for water pipe failure prediction.
A Comparison Study of Water Pipe Failure Prediction Models
Water leakages have been a major problem for water supply companies, one of the main causes of this problem is pipe failure in the water supply network. The risk prediction models of the pipe failure are also constantly being improved to determine the location of these leaks accurately and quickly. The statistical model is the Logistic Regression model (LR) used for failure prediction in groups of pipes. Machine Learning approaches, particularly the Decision Tree model (DT) and the Artificial Neural Network model (ANN) are compared in predicting individual pipe failure. In this paper, we proposed applying these three models to predict pipe failure for the DMA17 water supply network in ward 17, Go Vap District, Ho Minh city. Using Area Under the Curve (AUC) value to evaluate the modeling results, comparing this value showed that the ANN was the most suitable for water pipe failure prediction.
A Comparison Study of Water Pipe Failure Prediction Models
Lecture Notes in Civil Engineering
Reddy, J. N. (editor) / Wang, Chien Ming (editor) / Luong, Van Hai (editor) / Le, Anh Tuan (editor) / Pham, Thi Minh Lanh (author) / Nguyen, Quang Truong (author)
2022-09-21
9 pages
Article/Chapter (Book)
Electronic Resource
English
Pipe failure , Predicting model , Water supply network , Artificial neural network , Regression logistic model Energy , Sustainable Architecture/Green Buildings , Structural Materials , Geotechnical Engineering & Applied Earth Sciences , Building Construction and Design , Construction Management , Environmental Policy , Engineering
Water pipe failure prediction using AutoML
| Emerald Group Publishing | 2020
| Taylor & Francis Verlag | 2012
| British Library Online Contents | 2012