Akbar ShirzadMir Jafar Sadegh SafariShirzad, AkbarSafari, Mir Jafar Sadegh2025-10-0620191573062X, 174490061573-062X1744-900610.1080/1573062X.2020.17133842-s2.0-85078424873https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078424873&doi=10.1080%2F1573062X.2020.1713384&partnerID=40&md5=02df3bba990ce66834bd19ac6f5478f9https://gcris.yasar.edu.tr/handle/123456789/9353https://doi.org/10.1080/1573062X.2020.1713384This paper presents the results of a comparison between multivariate adaptive regression splines (MARS) and random forest (RF) techniques in pipe failure prediction in two water distribution networks. In this regard pipe diameter pipe length pipe installation depth pipe age and average hydraulic pressure are considered as input variables. Results show that the RF outperforms the MARS which is found as an accurate pipe failure rate predictor. The proposed models are further evaluated through dividing the data into three parts of lower medium and higher pipe failure rate values. According to the equations produced by MARS technique three variables of pipe diameter pipe age and average hydraulic pressure are distinguished as the most effective variables in predicting pipe failure rate in the first case study. Four variables of pipe diameter pipe length pipe age and average hydraulic pressure are determined as the most effective variables in the second case study. © 2020 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessMultivariate Adaptive Regression Splines, Pipe Failure Rate, Prediction Model, Random Forest, Water Distribution Network, Failure Analysis, Hydraulic Structure, Multivariate Analysis, Pipeline, Prediction, Pressure, Water Supplyfailure analysis, hydraulic structure, multivariate analysis, pipeline, prediction, pressure, water supplyMultivariate Adaptive Regression SplinesPrediction ModelRandom ForestWater Distribution NetworkPipe Failure RateArticle