Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models
The state of watermain systems is intrinsically linked to climate factors such as fluctuations in temperature and variations in rainfall. However, the integration of these climate-related factors into watermain failure prediction models, with a specific focus on climate change impacts, remains insuf...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/35 |
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| author | Melica Khashei Fatemeh Boloukasli ahmadgourabi Rebecca Dziedzic |
| author_facet | Melica Khashei Fatemeh Boloukasli ahmadgourabi Rebecca Dziedzic |
| author_sort | Melica Khashei |
| collection | DOAJ |
| description | The state of watermain systems is intrinsically linked to climate factors such as fluctuations in temperature and variations in rainfall. However, the integration of these climate-related factors into watermain failure prediction models, with a specific focus on climate change impacts, remains insufficiently explored. In response to these challenges, this research incorporates the potential effects of climate change on the frequency of watermain breaks by utilizing machine learning techniques, including K-Nearest Neighbours, Random Forest, Artificial Neural Network, and Extreme Gradient Boosting. By leveraging projected climate trends, the models provide actionable intelligence that can inform the development of more robust maintenance and rehabilitation strategies. |
| format | Article |
| id | doaj-art-da36343b29b44d74b53495fd357ccdce |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-da36343b29b44d74b53495fd357ccdce2025-08-20T02:42:42ZengMDPI AGEngineering Proceedings2673-45912024-09-016913510.3390/engproc2024069035Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction ModelsMelica Khashei0Fatemeh Boloukasli ahmadgourabi1Rebecca Dziedzic2Building, Civil and Environmental Engineering Department, Concordia University, Montreal, QC H3G 2W1, CanadaBuilding, Civil and Environmental Engineering Department, Concordia University, Montreal, QC H3G 2W1, CanadaBuilding, Civil and Environmental Engineering Department, Concordia University, Montreal, QC H3G 2W1, CanadaThe state of watermain systems is intrinsically linked to climate factors such as fluctuations in temperature and variations in rainfall. However, the integration of these climate-related factors into watermain failure prediction models, with a specific focus on climate change impacts, remains insufficiently explored. In response to these challenges, this research incorporates the potential effects of climate change on the frequency of watermain breaks by utilizing machine learning techniques, including K-Nearest Neighbours, Random Forest, Artificial Neural Network, and Extreme Gradient Boosting. By leveraging projected climate trends, the models provide actionable intelligence that can inform the development of more robust maintenance and rehabilitation strategies.https://www.mdpi.com/2673-4591/69/1/35watermain failureclimate changemachine learning |
| spellingShingle | Melica Khashei Fatemeh Boloukasli ahmadgourabi Rebecca Dziedzic Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models Engineering Proceedings watermain failure climate change machine learning |
| title | Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models |
| title_full | Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models |
| title_fullStr | Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models |
| title_full_unstemmed | Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models |
| title_short | Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models |
| title_sort | predicting the future failures of urban water systems integrating climate change and machine learning prediction models |
| topic | watermain failure climate change machine learning |
| url | https://www.mdpi.com/2673-4591/69/1/35 |
| work_keys_str_mv | AT melicakhashei predictingthefuturefailuresofurbanwatersystemsintegratingclimatechangeandmachinelearningpredictionmodels AT fatemehboloukasliahmadgourabi predictingthefuturefailuresofurbanwatersystemsintegratingclimatechangeandmachinelearningpredictionmodels AT rebeccadziedzic predictingthefuturefailuresofurbanwatersystemsintegratingclimatechangeandmachinelearningpredictionmodels |