Journal
URBAN WATER JOURNAL
Volume 16, Issue 2, Pages 136-145Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/1573062X.2019.1637004
Keywords
Data-driven models; machine learning; pipe burst rate; water distribution systems
Categories
Ask authors/readers for more resources
The water distribution network is one of the most expensive parts of a water supply system. The fundamental variables of a network, material, diameter, length, age, and the hydraulic pressure of pipes are the factors that affect the pipe burst rate (PBR). Establishing a relationship among the burst rate and these factors is an important step to assess the conditions governing the network and preventing significant water leakage. Implementing the data-driven approach in PBR prediction is an effective method to find the relationship. In the present study, Grasshopper Optimization Algorithm-based Support Vector Regression (GOA-SVR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) have been developed to predict PBR in an urban area. The results show that the GPR model outperforms other methods. Furthermore, the sensitivity analysis indicates that the pipe age has a negative effect on PBR modeling while the pipe length is the most relevant variable.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available