Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Abstract

The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

An evolution of statistical pipe failure models for drinking water networks: a targeted review

“…However, ensemble models significantly improve accuracy (Hastie et al 2009) but are computationally expensive. With computational advances and quicker processing speeds, ensemble methods are more common and have shown better accuracy, even compared to several other data-driven pipe failure models (Chen et al 2017;Giraldo-González & Rodríguez 2020). Tree ensemble models are simple in their approach and do not require mathematical pre-processing steps, and for this reason, they are an attractive machine learning models that is easily accessible (Winkler et al 2018).…”

Artificial Neural Networks to Forecast Failures in Water Supply Pipes

“…Finally, this result attained by our study is compared with those obtained in two previous ones (Table 5). In [2], an ANN (among other models) is used to predict pipe failures in a medium-sized Colombian city. The model is independently applied to asbestos cement and PVC pipes.…”

“…Intelligent predictive systems are models and algorithms that provide valuable information about the future performance of a system, serving as support for decision-making. In a recent study [2], researchers compare the performances of statistical models predicting failure rates in groups of pipes and machine-learning algorithms forecasting individual pipe failure rates. This work includes some of the most popular statistical models, as linear regression, Poisson regression and Evolutionary Polynomial Regression (EPR).…”

Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

“…Applications of SVM both for regression and classification are numerous in different sectors. In hydraulics and hydrology, examples include pipe failure detection in water distribution networks [36], prediction of urban water demand [37] rainfall-runoff modelling [38], flood forecasting [39], as well as reliability analysis [40][41][42] and dam safety [4,5,8,9,43,44].…”