Title: Estimating Colebrook-White Friction Factor Using Tree-Based Machine Learning Models
Book series: Lecture Notes in Networks and Systems (LNNS)
Proceeding: Latest Advancements in Mechanical Engineering
Conference: the 3rd International Symposium on Industrial Engineering and Automation 2024 (ISIEA 2024)
Conference location: Bolzano, Italy
Year: 2024
Volume number: 1124
Page numbers: 270–279
DOI: 10.1007/978-3-031-70462-8_26
Abstract: Colebrook-White friction factor is the common hydraulic roughness coefficient used in water supply systems. Analysis, design, and management of water distribution networks basically rely on an adequate estimation of friction factor. In turbulent flows, it depends on the Reynolds number and the relative roughness of pipes. This study assesses the performances of four tree-based Machine Learning (ML) models, including Decision Tree (DT) Regression, Adaboost, Gradient Boosting Regressor (GBR), and XGBoost. Their performances were compared with the equation of Swamee-Jain, which is utilized in EPANET hydraulic solver. For the comparative analysis, a reliable database comprising more than one million data points in the turbulent flow zone. Based on the results, GBR and DT performed better than the equation of Swamee-Jain. The improvement made in friction factor estimations using ML-based suggests further studies on the topic. Thus, future studies can be conducted by implementing ML-based estimator in hydraulic software for simulating pipe networks.
Keywords: Friction factor; Colebrook-White; Machine learning; water supply; decision tree; Gradient Boosting; XGBoost