Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems

References

• Abokifa, A.A.A., and L. Sela. 2019. “Identification of Spatial Patterns in Water Distribution Pipe Failure Data Using Spatial Autocorrelation Analysis.” Journal of Water Resources Planning and Management 145 (12): 04019057. doi:10.1061/(ASCE)WR.1943-5452.0001135.
   Web of Science ®Google Scholar
• Alizadeh, Z., J. Yazdi, S. Mohammadiun, K. Hewage, and R. Sadiq. 2019. “Evaluation of Data Driven Models for Pipe Burst Prediction in Urban Water Distribution Systems.” Urban Water Journal 16 (2): 136–145. doi:10.1080/1573062X.2019.1637004.
   Web of Science ®Google Scholar
• Almheiri, Z., M. Meguid, and T. Zayed. 2020. “An Approach to Predict the Failure of Water Mains under Climatic Variations.” International Journal of Geosynthetics and Ground Engineering 6 (4): 1–16.
   Web of Science ®Google Scholar
• Almheiri, Z., M. Meguid, and T. Zayed. 2021. “Failure Modeling of Water Distribution Pipelines Using meta-learning Algorithms.” Water Research 205: 117680. doi:10.1016/j.watres.2021.117680.
   PubMed Web of Science ®Google Scholar
• Altman, N.S. 1992. “An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression.” American Statistician 46 (3): 175–185.
   Web of Science ®Google Scholar
• American Society of Civil Engineers. 2017. Infrastructure Report Card: Drinking Water. Reston, VA: American Society of Civil Engineers (ASCE) Publisher.
   Google Scholar
• American Water Works Association. 2012. Buried No Longer: Confronting America’s Water Infrastructure Challenge. Denver, CO: American Water Works Association (AWWA) Publisher.
   Google Scholar
• Anselin, L. 1995. “Local Indicators of Spatial Association—LISA.” Geographical Analysis 27 (2): 93–115. doi:10.1111/j.1538-4632.1995.tb00338.x.
   Web of Science ®Google Scholar
• Anselin, L. 2002. “Under the Hood Issues in the Specification and Interpretation of Spatial Regression Models.” Agricultural Economics 27 (3): 247–267. doi:10.1111/j.1574-0862.2002.tb00120.x.
   Web of Science ®Google Scholar
• Aslani, B., S. Mohebbi, and H. Axthelm. 2021. “Predictive Analytics for Water Main Breaks Using Spatiotemporal Data“. Urban Water Journal. 18 (6): 433–448. doi:10.1080/1573062X.2021.1893363.
   Web of Science ®Google Scholar
• Barton, N.A., S.H. Hallett, and S.R. Jude. 2022. “The Challenges of Predicting Pipe Failures in Clean Water Networks: A View from Current Practice.” Water Supply 22 (1): 527–541. doi:10.2166/ws.2021.255.
   Google Scholar
• Benjamini, Y., and Y. Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 57 (1): 289–300.
   Web of Science ®Google Scholar
• Berardi, L., O. Giustolisi, Z. Kapelan, and D.A. Savic. 2008. “Development of Pipe Deterioration Models for Water Distribution Systems Using EPR.” Journal of Hydroinformatics 10 (2): 113. doi:10.2166/hydro.2008.012.
   Web of Science ®Google Scholar
• Boland, J., D.D. Baumann, and B. Dziegielewski. 1981. An Assessment of Municipal and Industrial Water Use Forecasting Approaches. Carbondale IL: Planning And Management Consultants Ltd.
   Google Scholar
• Chen, T.Y.J., and S.D. Guikema. 2020. “Prediction of Water Main Failures with the Spatial Clustering of Breaks.” Reliability Engineering and System Safety. 203: 107108.
   Web of Science ®Google Scholar
• Chi, G., and J. Zhu. 2008. “Spatial Regression Models for Demographic Analysis.” Population Research and Policy Review 27 (1): 17–42. doi:10.1007/s11113-007-9051-8.
   Web of Science ®Google Scholar
• Christodoulou, S., A. Gagatsis, A. Agathokleous, S. Xanthos, and S. Kranioti. 2012. “Urban Water Distribution Network Asset Management Using Spatio-Temporal Analysis of Pipe-Failure Data.” 14th International Conference on Computing in Civil and Building Engineering 27: 29.
   Google Scholar
• de Oliveira, D.P., J.H. Garrett, and L. Soibelman. 2011a. “A density-based Spatial Clustering Approach for Defining Local Indicators of Drinking Water Distribution Pipe Breakage.” Advanced Engineering Informatics 25 (2): 380–389. doi:10.1016/j.aei.2010.09.001.
   Web of Science ®Google Scholar
• de Oliveira, D.P., D.B. Neill, J.H. Garrett, and L. Soibelman. 2011b. “Detection of Patterns in Water Distribution Pipe Breakage Using Spatial Scan Statistics for Point Events in a Physical Network.” Journal of Computing in Civil Engineering 25 (1): 21–30. doi:10.1061/(ASCE)CP.1943-5487.0000079.
   Web of Science ®Google Scholar
• Dormann, C.F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, Jaime R. García. Marquéz, et al. 2013. “Collinearity: A Review of Methods to Deal with It and A Simulation Study Evaluating Their Performance.” Ecography (Cop.) 36 (1): 27–46. doi:10.1111/j.1600-0587.2012.07348.x.
   Web of Science ®Google Scholar
• Ercumen, A., J.S. Gruber Jr, and J M. Colford. 2014. “Water Distribution System Deficiencies and Gastrointestinal Illness: A Systematic Review and Meta-Analysis.” Environmental Health Perspectives 122 (7): 651–661. doi:10.1289/ehp.1306912.
   PubMed Web of Science ®Google Scholar
• Fan, Xudong, Xiaowei Wang, Xijin Zhang, and Xiong Bill Yu. 2022. “Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors”. Reliability Engineering & System Safety. 219: doi:10.1016/j.ress.2021.108185.
   Google Scholar
• Fan, Xudong, Xijin Zhang, and Xiong Yu. 2022. “A graph convolution network–deep reinforcement learning model for resilient water distribution network repair decisions”. Computer–Aided Civil and Infrastructure Engineering. 37 (12): 1547–1565.
   Google Scholar
• Florax, R., and H. Folmer. 1992. “Specification and Estimation of Spatial Linear Regression Models: Monte Carlo Evaluation of pre-test Estimators.” Regional Science and Urban Economics 22 (3): 405–432. doi:10.1016/0166-0462(92)90037-2.
   Web of Science ®Google Scholar
• Folkman, S. 2018. Water Main Break Rates in the USA and Canada: A Comprehensive Study. Logan, Utah: Utah State Univ.
   Google Scholar
• Hering, J.G., T.D. Waite, R.G. Luthy, J.E. Drewes, and D.L. Sedlak. 2013. “A Changing Framework for Urban Water Systems.” Environmental Science & Technology 47 (19): 10721–10726. doi:10.1021/es4007096.
   PubMed Web of Science ®Google Scholar
• Hoerl, A.E., and R.W. Kennard. 1970. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12 (1): 55–67. doi:10.1080/00401706.1970.10488634.
   Web of Science ®Google Scholar
• Hunter, J.D. 2007. “Matplotlib: A 2D Graphics Environment.” Computing in Science & Engineering 9 (3): 90. doi:10.1109/MCSE.2007.55.
   Web of Science ®Google Scholar
• James, G., D. Witten, T. Hastie, and R. Tibshirani. 2013. An Introduction to Statistical Learning. New York: Springer.
   Google Scholar
• Jenkins, L., S. Gokhale, and M. McDonald. 2015. “Comparison of Pipeline Failure Prediction Models for Water Distribution Networks with Uncertain and Limited Data.” Journal of Pipeline Systems Engineering and Practice 6 (2): 04014012. doi:10.1061/(ASCE)PS.1949-1204.0000181.
   Web of Science ®Google Scholar
• Kabir, G., S. Tesfamariam, J. Loeppky, and R. Sadiq. 2015. “Integrating Bayesian Linear Regression with Ordered Weighted Averaging: Uncertainty Analysis for Predicting Water Main Failures.” ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 1 (3): 04015007. doi:10.1061/AJRUA6.0000820.
   Google Scholar
• Kakoudakis, K., K. Behzadian, R. Farmani, and D. Butler. 2017. “Pipeline Failure Prediction in Water Distribution Networks Using Evolutionary Polynomial Regression Combined with K -means Clustering.” Urban Water Journal 14 (7): 737–742. doi:10.1080/1573062X.2016.1253755.
   Web of Science ®Google Scholar
• Kakoudakis, K., R. Farmani, and D. Butler. 2018. “Pipeline Failure Prediction in Water Distribution Networks Using Weather Conditions as Explanatory Factors.” Journal of Hydroinformatics 20 (5): 1191–1200. doi:10.2166/hydro.2018.152.
   Web of Science ®Google Scholar
• Kimutai, E., G. Betrie, R. Brander, R. Sadiq, and S. Tesfamariam. 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. doi:10.1061/(ASCE)PS.1949-1204.0000196.
   Web of Science ®Google Scholar
• Kleiner, Y., and B. Rajani. 2001. “Comprehensive Review of Structural Deterioration of Water Mains: Statistical Models.” Urban Water 3 (3): 131–150. doi:10.1016/S1462-0758(01)00033-4.
   Google Scholar
• Konstantinou, C., and I. Stoianov. 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. doi:10.1080/1573062X.2020.1800758.
   Web of Science ®Google Scholar
• Laucelli, D., B. Rajani, Y. Kleiner, and O. Giustolisi. 2014. “Study on Relationships between climate-related Covariates and Pipe Bursts Using evolutionary-based Modelling.” Journal of Hydroinformatics 16 (4): 743–757. doi:10.2166/hydro.2013.082.
   Web of Science ®Google Scholar
• Legendre, P. 1993. “Spatial Autocorrelation: Trouble or New Paradigm?” Ecology 74 (6): 1659–1673. doi:10.2307/1939924.
   Web of Science ®Google Scholar
• Liaw, A., and M. Wiener. 2002. “Classification and Regression by randomForest.” R News 2 (3): 18–22.
   Google Scholar
• McKinney, W. 2010. “Data Structures for Statistical Computing in Python.” Proceedings of the 9th Python in Science Conference. Austin, TX, pp. 51–56.
   Google Scholar
• Nishiyama, M., and Y. Filion. 2013. “Review of Statistical Water Main Break Prediction Models.” Canadian Journal of Civil Engineering 40 (10): 972–979. doi:10.1139/cjce-2012-0424.
   Web of Science ®Google Scholar
• Obringer, R., and R. Nateghi. 2018. “Predicting Urban Reservoir Levels Using Statistical Learning Techniques.” Scientific Reports 8 (1): 1–9. doi:10.1038/s41598-018-23509-w.
   PubMedGoogle Scholar
• Oliveira, D., J.H. Garrett, and L. Soibelman. 2009. “Spatial Clustering Analysis of Water Main Break Events.” In Computing in Civil Engineering. 338–347. Reston, VA: American Society of Civil Engineers (ASCE).
   Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg. 2011. “Scikit-learn: Machine Learning in Python.” Journal of Machine Learning Research 12 (Oct): 2825–2830.
   Google Scholar
• Rajani, B., and Y. Kleiner. 2001. “Comprehensive Review of Structural Deterioration of Water Mains: Physically Based Models.” Urban Water 3 (3): 151–164. doi:10.1016/S1462-0758(01)00032-2.
   Google Scholar
• Rey, S.J., and L. Anselin. 2010. PySAL: A Python Library of Spatial Analytical Methods, In: Handbook of Applied Spatial Analysis, 175–193. Berlin/Heidelberg, Germany: Springer.
   Google Scholar
• Rifaai, T.M., A.A. Abokifa, and L. Sela. 2022. “Integrated Approach for Pipe Failure Prediction and Condition Scoring in Water Infrastructure Systems.” Reliability Engineering & System Safety 220: 108271. doi:10.1016/j.ress.2021.108271.
   Web of Science ®Google Scholar
• Scheidegger, A., J.P. Leitão, and L. Scholten. 2015. “Statistical Failure Models for Water Distribution Pipes - A Review from A Unified Perspective.” Water Research 83: 237–247. doi:10.1016/j.watres.2015.06.027.
   PubMed Web of Science ®Google Scholar
• Shin, H., K. Kobayashi, J. Koo, and M. Do. 2016. “Estimating Burst Probability of Water Pipelines with a Competing Hazard Model.” Journal of Hydroinformatics 18 (1): 126–135. doi:10.2166/hydro.2015.016.
   Web of Science ®Google Scholar
• Shirzad, A., and M.J.S. Safari. 2020. “Pipe Failure Rate Prediction in Water Distribution Networks Using Multivariate Adaptive Regression Splines and Random Forest Techniques.” Urban Water Journal 16 (9): 653–661. doi:10.1080/1573062X.2020.1713384.
   Web of Science ®Google Scholar
• Smola, A.J., and B. Schölkopf. 2004. “A Tutorial on Support Vector Regression.” Statistics and Computing 14 (3): 199–222. doi:10.1023/B:STCO.0000035301.49549.88.
   Web of Science ®Google Scholar
• Specht, D.F. 1991. “A General Regression Neural Network.” IEEE Transactions on Neural Networks 2 (6): 568–576. doi:10.1109/72.97934.
   PubMed Web of Science ®Google Scholar
• St. Clair, A.M., and S. Sinha. 2012. “State-of-the-technology Review on Water Pipe Condition, Deterioration and Failure Rate Prediction Models!” Urban Water Journal 9 (2): 85–112. doi:10.1080/1573062X.2011.644566.
   Web of Science ®Google Scholar
• Stone, S.L., E.J. Dzuray, D. Meisegeier, A.S. Dahlborg, M. Erickson, and A.N. Tafuri. 2002. “Decision-support Tools for Predicting the Performance of Water Distribution and Wastewater Collection Systems.” U.S. Environmental Protection Agency, Office of Research and Development.
   Google Scholar
• Tabesh, M., J. Soltani, R. Farmani, and D. Savic. 2009. “Assessing Pipe Failure Rate and Mechanical Reliability of Water Distribution Networks Using data-driven Modeling.” Journal of Hydroinformatics 11 (1): 1–17. doi:10.2166/hydro.2009.008.
   Web of Science ®Google Scholar
• Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B: Statistical Methodology 58 (1): 267–288.
   Web of Science ®Google Scholar
• Van Der Walt, S., S.C. Colbert, and G. Varoquaux. 2011. “The NumPy Array: A Structure for Efficient Numerical Computation.” Computing in Science & Engineering 13 (2): 22. doi:10.1109/MCSE.2011.37.
   Web of Science ®Google Scholar
• Wang, Y., T. Zayed, and O. Moselhi. 2009. “Prediction Models for Annual Break Rates of Water Mains.” Journal of Performance of Constructed Facilities 23 (1): 47–54. doi:10.1061/(ASCE)0887-3828(2009)23:1(47).
   Web of Science ®Google Scholar
• Wilson, D., Y. Filion, and I. Moore. 2015. “State-of-the-art Review of Water Pipe Failure Prediction Models and Applicability to large-diameter Mains.” Urban Water Journal 14 (2): 173–184. doi:10.1080/1573062X.2015.1080848.
   Web of Science ®Google Scholar
• Xu, Q., Q. Chen, W. Li, and J. Ma. 2011. “Pipe Break Prediction Based on Evolutionary data-driven Methods with Brief Recorded Data.” Reliability Engineering & System Safety 96 (8): 942–948. doi:10.1016/j.ress.2011.03.010.
   Web of Science ®Google Scholar
• Yamijala, S., S.D. Guikema, and K. Brumbelow. 2009. “Statistical Models for the Analysis of Water Distribution System Pipe Break Data.” Reliability Engineering & System Safety 94 (2): 282–293. doi:10.1016/j.ress.2008.03.011.
   Web of Science ®Google Scholar
• Zou, H., and T. Hastie. 2005. “Regression and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67 (2): 301–320. doi:10.1111/j.1467-9868.2005.00503.x.
   Web of Science ®Google Scholar