Pipeline failure prediction in water distribution networks using evolutionary polynomial regression combined with K-means clustering

Abstract

This paper presents a new approach for improving pipeline failure predictions by combining a data-driven statistical model, i.e. evolutionary polynomial regression (EPR), with K-means clustering. The EPR is used for prediction of pipe failures based on length, diameter and age of pipes as explanatory factors. Individual pipes are aggregated using their attributes of age, diameter and soil type to create homogenous groups of pipes. The created groups were divided into training and test datasets using the cross-validation technique for calibration and validation purposes respectively. The K-means clustering is employed to partition the training data into a number of clusters for individual EPR models. The proposed approach was demonstrated by application to the cast iron pipes of a water distribution network in the UK. Results show the proposed approach is able to significantly reduce the error of pipe failure predictions especially in the case of a large number of failures. The prediction models were used to calculate the failure rate of individual pipes for rehabilitation planning.