Artificial Neural Networks to Forecast Failures in Water Supply Pipes
| Autor: | Pablo Cortés, Alicia Robles-Velasco, Jesús Muñuzuri, Cristóbal Ramos-Salgado |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Geography Planning and Development 0211 other engineering and technologies Water supply TJ807-830 020101 civil engineering 02 engineering and technology Management Monitoring Policy and Law TD194-195 Renewable energy sources 0201 civil engineering Software 021105 building & construction artificial neural networks water supply system pipe failures prediction machine learning sampling methods GE1-350 Training set Artificial neural network Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry Reliability engineering Environmental sciences business |
| Zdroj: | Sustainability, Vol 13, Iss 8226, p 8226 (2021) Sustainability; Volume 13; Issue 15; Pages: 8226 |
| ISSN: | 2071-1050 |
| Popis: | The water supply networks of many countries are experiencing a drastic increase in the number of pipe failures. To reverse this tendency, it is essential to optimise the replacement plans of pipes. For this reason, companies demand pioneering techniques to predict which pipes are more prone to fail. In this study, an Artificial Neural Network (ANN) is designed to classify pipes according to their predisposition to fail based on physical and operational input variables. In addition, the usefulness and effectiveness of two sampling methods, under-sampling and over-sampling, are analysed. The implementation of the model is done using the open-source software Weka, which is specialised in machine-learning algorithms. The system is tested with a database from a real water network in Spain, obtaining high-accurate results. It is verified that the balance of the training set is imperative to increase the predictions’ accurateness. Furthermore, under-sampling prioritises true positive rates, whereas over-sampling makes the system learn to predict failures and non-failures with the same precision. |
| Databáze: | OpenAIRE |
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