A review of leakage detection strategies for pressurised pipeline in steady-state

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 Bogota (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.

https://www.mdpi.com/2073-4441/12/4/1153/pdf

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

Machine Learning based Water Pipe Failure Prediction: The Effects of Engineering, Geology, Climate and Socio-Economic Factors

Remineralization of desalinated water: Methods and environmental impact

Underground water pipes deteriorate under the influence of various physical, mechanical, environmental, and social factors. Reliable pipe failure prediction is essential for a proactive management strategy of the water supply network (WSN), which is challenging for the conventional physics-based model. This study applied data-driven machine learning (ML) models to predict water pipe failures by leveraging the historical maintenance data heritage of a large water supply network. A multi-source data-aggregation framework was firstly established to integrate various contributing factors to underground pipe deterioration. The framework defined criteria for the integration of various data sources including the historical pipe break dataset, soil type dataset, topographical dataset, census dataset, and climate dataset. Based on the data, five ML algorithms, including LightGBM, Artificial Neural Network, Logistic Regression, K-Nearest Neighbors, and Support Vector Classification are developed for pipe failure prediction. LightGBM was found to achieve the best performance. The relative importance of major contributing factors on the water pipe failures was analyzed. Interestingly, the socioeconomic factors of a community are found to affect the probability of pipe failures. This study indicates that data-driven analysis that integrates the Machine Learning (ML) techniques and the proposed data integration framework has the potential to support reliable decision-making in WSN management. 

Timothy Francis Acland

Papers

Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks.