Water pipe failure prediction using AutoML

Abstract

Purpose Owing to the consumption of considerable resources in developing physical pipe prediction models and the fact that the statistical models cannot fit the failure records perfectly, the purpose of this paper is to use data mining method to analyze and predict the risks of water pipe failure via considering attributes and location of pipes in historical failure records. One of the Automatized Machine Learning (AutoML) methods, tree-based pipeline optimization technique (TPOT) was used as the key data mining technique in this research. Design/methodology/approach By considering pipeline attributes, environmental factors and historical pipeline broke/breaks records, a water pipeline failure prediction method is proposed in this research. Regression analysis, genetic algorithm, machine learning, data mining approaches are used to analyze and predict the probability of pipeline failure. TPOT was used as the key data mining technique. A case study was carried out in a specific area in China to investigate the relationships between pipeline broke/breaks and relevant parameters, such as pipeline age, materials, diameter, pipeline density and so on. Findings By integrating the prediction models for individual pipelines and small research regions, a prediction model is developed to describe the probability of water pipe failures and validated by real data. A high fitting degree is achieved, which means a good potential of using the proposed method in reality as a guideline for identifying areas with high risks and taking proactive measures and optimizing the resources allocation for water supply companies. Originality/value Different models are developed to have better prediction on regional or individual pipeline. A comparison between the predicted values with real records has shown that a preliminary model has a good potential in predicting the future failure risks.

A Machine learning-based approach to determining stress in rails

“…56 AutoML was used to predict water pipe failures as well. 57 In this new added step, AutoML Tables was calibrated using the training data generated numerically. The new models took 2 hours to be trained under the same 2BC and 3BC datasets discussed in the previous sections.…”

Teaching-Learning-Based Optimization of Neural Networks for Water Supply Pipe Condition Prediction

“…The GRNN outperformed the other models in terms of the evaluation metrics. To forecast the pipe failure probability, Zhang and Ye [40] compared the regression analysis, machine learning, genetic algorithms, and data mining approaches. A tree-based optimization approach was proposed as a data mining technique.…”

Case Study for Predicting Failures in Water Supply Networks Using Neural Networks

“…Predicting future failures in water supply networks is a complex and computationally expensive task, as it involves processing data with high volume and dimensionality [9]. In this context, three approaches stand out for predicting failures in this type of network: predictions based on physical models of the pipeline; Predictions based on statistical models; And predictions based on Machine Learning models.…”

“…Such data may be scarce in terms of quantity and variety (diversity of variables) because they are highly sensitive for companies, and access to them may be limited. Data on network structure, historical failure records, spatial and meteorological data, for example, are private to water supply companies and are considered strategic for their business models, which complicates access [9,11].…”