Framework for Predicting Water Main Breaks in the Face of Climate Change
Publication: World Environmental and Water Resources Congress 2024
ABSTRACT
Water distribution systems, crucial for sustainable communities, face increased failure risks as they age and undergo operational and environmental changes, leading to issues like water loss, sanitation problems, infrastructure damage, and service disruptions. Climate change heightens the risk of water main failure by altering weather patterns, including precipitation and temperature extremes. This research highlights the impact of climate factors like temperature fluctuations and rainfall deficits on predicting water main failures. A deep learning-based predictive model using long short-term memory (LSTM) networks is developed to account for climate change. Water main and break records, combined with climate data including temperature and rainfall, are used to test the method’s sensitivity to different climate scenarios. Its effectiveness is validated through a case study in Saskatoon, Canada. The model exhibits moderate accuracy, evidenced by a mean absolute error (MAE) between 0.040 and 0.192. Results indicate that cast iron pipes are more vulnerable to future climate scenarios with colder temperatures, while the overall system and asbestos cement pipes are likely to face increased failures in scenarios with higher temperatures.
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REFERENCES
Ahmad, T., Shaban, I. A., and Zayed, T. (2023). A review of climatic impacts on water main deterioration. Urban Climate, 49. https://doi.org/10.1016/j.uclim.2023.101552.
Almheiri, Z., Meguid, M., and Zayed, T. (2020). An Approach to Predict the Failure of Water Mains Under Climatic Variations. International Journal of Geosynthetics and Ground Engineering, 6(4). https://doi.org/10.1007/s40891-020-00237-8.
Aslani, B., Mohebbi, S., and Axthelm, H. (2021). Predictive analytics for water main breaks using spatiotemporal data. Urban Water Journal, 18(6), 433–448. https://doi.org/10.1080/1573062X.2021.1893363.
AWWA. (2012). Buried No Longer: Confronting America’s Water Infrastructure Challenge.
Barton, N. A., Farewell, T. S., Hallett, S. H., and Acland, T. F. (2019a). Improving pipe failure predictions: Factors effecting pipe failure in drinking water networks. Water Research, 164. https://doi.org/10.1016/j.watres.2019.114926.
Barton, N. A., Farewell, T. S., Hallett, S. H., and Acland, T. F. (2019b). Improving pipe failure predictions: Factors effecting pipe failure in drinking water networks. Water Research, 164, 114926. https://doi.org/10.1016/j.watres.2019.114926.
Bruaset, S., and Sægrov, S. (2018). An analysis of the potential impact of climate change on the structural reliability of drinking water pipes in cold climate regions. Water (Switzerland), 10(4). https://doi.org/10.3390/w10040411.
Chan, D., Gallage, C. P. K., Rajeev, P., and Kodikara, J. (2015). Field performance of in-service cast iron water reticulation pipe buried in reactive clay. Canadian Geotechnical Journal, 52(11), 1861–1873. https://doi.org/10.1139/cgj-2014-0531.
Chen, T. Y.-J., Beekman, J. A., David Guikema, S., and Shashaani, S. (2019). Statistical Modeling in Absence of System Specific Data: Exploratory Empirical Analysis for Prediction of Water Main Breaks. Journal of Infrastructure Systems, 25(2). https://doi.org/10.1061/(asce)is.1943-555x.0000482.
Chik, L., Albrecht, D., and Kodikara, J. (2018). Modeling failures in water mains using the minimum monthly antecedent precipitation index. Journal of Water Resources Planning and Management, 144(4), 06018004.
Dai, A., Zhao, T., and Chen, J. (2018). Climate Change and Drought: a Precipitation and Evaporation Perspective. In Current Climate Change Reports (Vol. 4, Issue 3, pp. 301–312). Springer. https://doi.org/10.1007/s40641-018-0101-6.
Demissie, G., Tesfamariam, S., and Sadiq, R. (2017). Prediction of Pipe Failure by Considering Time-Dependent Factors: Dynamic Bayesian Belief Network Model. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3(4). https://doi.org/10.1061/ajrua6.0000920.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958.
Fan, X., Wang, X., Zhang, X., and Yu, X. B. (2022). Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors. Reliability Engineering and System Safety, 219. https://doi.org/10.1016/j.ress.2021.108185.
Fan, X., Zhang, X., and Yu, X. B. (2023). Uncertainty quantification of a deep learning model for failure rate prediction of water distribution networks. Reliability Engineering and System Safety, 236. https://doi.org/10.1016/j.ress.2023.109088.
Folkman, S. (2018). Water Main Break Rates In the USA and Canada: A Comprehensive Study Overall Pipe Breaks Up 27% In Six Years.
Fuchs-Hanusch, D., Friedl, F., Scheucher, R., Kogseder, B., and Muschalla, D. (2013). Effect of seasonal climatic variance on water main failure frequencies in moderate climate regions. Water Supply, 13(2), 435–446. https://doi.org/10.2166/ws.2013.033.
Gould, S., Boulaire, F., Marlow, D., and Kodikara, J. (2009). Understanding how the Australian climate can affect pipe failure. OzWater’09 Conference Proceedings, 1–8.
Harvey, R., McBean, E. A., and Gharabaghi, B. (2014). Predicting the Timing of Water Main Failure Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 140(4), 425–434. https://doi.org/10.1061/(asce)wr.1943-5452.0000354.
Hong, H. P. (1997). Reliability based optimal inspection and maintenance for pipeline under corrosion. Civil Engineering Systems, 14(4), 313–334.
Boyle, J., Cunningham, M., and Dekens, J. (2013). Climate Change Adaptation and Canadian Infrastructure. https://www.iisd.org/publications/report/climate-change-adaptation-and-canadian-infrastructure-review-literature.
Kakoudakis, K., Farmani, R., and Butler, D. (2018). Pipeline failure prediction in water distribution networks using weather conditions as explanatory factors. Journal of Hydroinformatics, 20(5), 1191–1200. https://doi.org/10.2166/hydro.2018.152.
Kimutai, E., Betrie, G., Brander, R., Sadiq, R., and Tesfamariam, S. (2015). Comparison of Statistical Models for Predicting Pipe Failures: Illustrative Example with the City of Calgary Water Main Failure. Journal of Pipeline Systems Engineering and Practice, 6(4), 04015005. https://doi.org/10.1061/(asce)ps.1949-1204.0000196.
Kleiner, Y., and Rajani, B. (2001). Comprehensive review of structural deterioration of water mains: statistical models. Urban Water, 3(3), 131–150. https://doi.org/10.1016/S1462-0758(01)00033-4.
Konstantinou, C., and Stoianov, I. (2020). A comparative study of statistical and machine learning methods to infer causes of pipe breaks in water supply networks. Urban Water Journal, 17(6), 534–548. https://doi.org/10.1080/1573062X.2020.1800758.
Lawrence, J., Blackett, P., and Cradock-Henry, N. A. (2020). Cascading climate change impacts and implications. Climate Risk Management, 29. https://doi.org/10.1016/j.crm.2020.100234.
Olmstead, S. M. (2014). Climate change adaptation and water resource management: A review of the literature. Energy Economics, 46, 500–509. https://doi.org/10.1016/j.eneco.2013.09.005.
Rajani, B., and Kleiner, Y. (2001). Comprehensive review of structural deterioration of water mains: physically based models. Urban Water, 3(3), 151–164. https://doi.org/10.1016/S1462-0758(01)00032-2.
Rajani, B., Kleiner, Y., and Sink, J.-E. (2012). Exploration of the relationship between water main breaks and temperature covariates. Urban Water Journal, 9(2), 67–84. https://doi.org/10.1080/1573062X.2011.630093.
Robles-Velasco, A., Cortés, P., Muñuzuri, J., and Onieva, L. (2020). Prediction of pipe failures in water supply networks using logistic regression and support vector classification. Reliability Engineering and System Safety, 196. https://doi.org/10.1016/j.ress.2019.106754.
Shi, F., Peng, X., Liu, Z., Li, E., and Hu, Y. (2020). A data-driven approach for pipe deformation prediction based on soil properties and weather conditions. Sustainable Cities and Society, 55. https://doi.org/10.1016/j.scs.2019.102012.
Snider, B., and McBean, E. A. (2020). Improving Urban Water Security through Pipe-Break Prediction Models: Machine Learning or Survival Analysis. Journal of Environmental Engineering, 146(3). https://doi.org/10.1061/(asce)ee.1943-7870.0001657.
Suter, L., Streletskiy, D., and Shiklomanov, N. (2019). Assessment of the cost of climate change impacts on critical infrastructure in the circumpolar Arctic. Polar Geography, 42(4), 267–286.
Tabesh, M., Soltani, J., Farmani, R., and Savic, D. (2009). Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling. Journal of Hydroinformatics, 11(1), 1–17. https://doi.org/10.2166/hydro.2009.008.
Van Houdt, G., Mosquera, C., and Nápoles, G. (2020). A review on the long short-term memory model. Artificial Intelligence Review, 53, 5929–5955.
Wilson, D., Filion, Y., and Moore, I. (2017). State-of-the-art review of water pipe failure prediction models and applicability to large-diameter mains. Urban Water Journal, 14(2), 173–184. https://doi.org/10.1080/1573062X.2015.1080848.
Wols, B. A., and Van Thienen, P. (2014). Modelling the effect of climate change induced soil settling on drinking water distribution pipes. Computers and Geotechnics, 55, 240–247. https://doi.org/10.1016/j.compgeo.2013.09.003.
Wols, B. A., and Van Thienen, P. (2016). Impact of climate on pipe failure: Predictions of failures for drinking water distribution systems. European Journal of Transport and Infrastructure Research, 16(1).
Wols, B. A., Vogelaar, A., Moerman, A., and Raterman, B. (2019). Effects of weather conditions on drinking water distribution pipe failures in the Netherlands. Water Science and Technology: Water Supply, 19(2), 404–416. https://doi.org/10.2166/ws.2018.085.
Yamijala, S., Guikema, S. D., and Brumbelow, K. (2009). Statistical models for the analysis of water distribution system pipe break data. Reliability Engineering and System Safety, 94(2), 282–293. https://doi.org/10.1016/j.ress.2008.03.011.
Zamenian, H., Mannering, F. L., Abraham, D. M., and Iseley, T. (2017). Modeling the Frequency of Water Main Breaks in Water Distribution Systems: Random-Parameters Negative-Binomial Approach. Journal of Infrastructure Systems, 23(2). https://doi.org/10.1061/(asce)is.1943-555x.0000336.
Information & Authors
Information
Published In
World Environmental and Water Resources Congress 2024
Pages: 1326 - 1338
History
Published online: May 16, 2024
ASCE Technical Topics:
- Analysis (by type)
- Climate change
- Climates
- Engineering fundamentals
- Engineering mechanics
- Environmental engineering
- Failure analysis
- Infrastructure
- Infrastructure vulnerability
- Material mechanics
- Material properties
- Materials engineering
- Measurement (by type)
- Temperature (by type)
- Temperature effects
- Temperature measurement
- Thermal properties
- Thermodynamics
- Water and water resources
- Water leakage and water loss
- Water management
- Water supply
- Water supply systems
Authors
Metrics & Citations
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