Pipe failure modelling for water distribution networks using boosted decision trees

References

• Ahmadi, M., Cherqui, F., Aubin, J.-B., & Le Gauffre, P. (2015). Sewer asset management: Impact of sample size and its characteristics on the calibration outcomes of a decision-making multivariate model. Urban Water Journal, 13(1), 41–56. doi:10.1080/1573062X.2015.1011668
   Web of Science ®Google Scholar
• Alegre, H., & Coelho, S.T. (2012). Infrastructure asset management of urban water systems. In A. Ostfeld (Ed.), Water supply system analysis – Selected topics (pp. 49–74). Rijeka, Croatia: InTech. doi:10.5772/52377
   Google Scholar
• Ana, E.V., & Bauwens, W. (2010). Modeling the structural deterioration of urban drainage pipes: The state-of-the-art in statistical methods. Urban Water Journal, 7(1), 47–59. doi:10.1080/15730620903447597
   Web of Science ®Google Scholar
• Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140. doi:10.1007/BF00058655
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. doi:10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Breiman, L., Friedman, J., Stone, C.J., & Olshen, R.A. (1984). Classification and regression trees. Boca Raton, FL: CRC Press.
   Google Scholar
• Christodoulou, S., & Deligianni, A. (2010). A neurofuzzy decision framework for the management of water distribution networks. Water Resources Management, 24(1), 139–156. doi:10.1007/s11269-009-9441-2
   Web of Science ®Google Scholar
• Cochran, W.G. (2007). Sampling techniques. New York City, NY: Wiley.
   Google Scholar
• Debón, A., Carrión, A., Cabrera, E., & Solano, H. (2010). Comparing risk of failure models in water supply networks using ROC curves. Reliability Engineering and System Safety, 95, 43–48. doi:10.1016/j.ress.2009.07.004
   Web of Science ®Google Scholar
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874. doi:10.1016/j.patrec.2005.10.010
   Web of Science ®Google Scholar
• Freund, Y., & Schapire, R. (1999). A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14, 771–780.
   Google Scholar
• Freund, Y., & Schapire, R.E., et al. (1996). Experiments with a new boosting algorithm. In Proceedings of the thirteenth international conference on machine learning (Vol. 96, pp. 148–156), Bari, Italy.
   Google Scholar
• Freund, Y., & Schapire, R.E.R. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. doi:10.1006/jcss.1997.1504
   Web of Science ®Google Scholar
• Friedl, F., Möderl, M., Rauch, W., Liu, Q., Schrotter, S., & Fuchs-Hanusch, D. (2012). Failure propagation for large-diameter transmission water mains using dynamic failure risk index. World Environmental and Water Resources Congress, 2012, 3082–3095. doi:10.1061/9780784412312.310
   Google Scholar
• Ghorbanian, V., Karney, B., & Guo, Y. (2016). Pressure standards in water distribution systems: Reflection on current practice with consideration of some unresolved issues. Journal of Water Resources Planning and Management, 142(8), 04016023.10.1061/(ASCE)WR.1943-5452.0000665
   Web of Science ®Google Scholar
• Giustolisi, O., Laucelli, D., & Savic, D. (2006). Development of rehabilitation plans for water mains replacement considering risk and cost-benefit assessment. Civil Engineering and Environmental Systems, 23(3), 175–190. doi:10.1080/10286600600789375
   Web of Science ®Google Scholar
• Harvey, R.R., & McBean, E.A. (2014). Predicting the structural condition of individual sanitary sewer pipes with random forests. Canadian Journal of Civil Engineering, 41(4), 294–303. doi:10.1139/cjce-2013-0431
   Web of Science ®Google Scholar
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. New York, NY: Springer. doi:10.1007/b94608
   Google Scholar
• Hosmer, D.W., & Lemeshow, S. (2000). Applied logistic regression. Hoboken, NJ: Wiley. doi:10.1002/0471722146
   Google Scholar
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. New York, NY: Springer. doi:10.1007/978-1-4614-7138-7
   Google Scholar
• Jilong, S., Ronghe, W., Junhui, P., Liang, C., & Chaohong, X. (2014). Decision tree classification model in water supply network. CUNY Academic Works. Retrieved from http://academicworks.cuny.edu/cc_conf_hic/65
   Google Scholar
• Kleiner, Y., & Rajani, B. (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
• Kotsiantis, S.B. (2013, April 29). Decision trees: A recent overview. Artificial Intelligence Review, 39, 261–283. Netherlands: Springer. doi:10.1007/s10462-011-9272-4
   Web of Science ®Google Scholar
• Lei, J., & Sægrov, S. (1998). Statistical approach for describing failures and lifetimes of water mains. Water Science and Technology, 38(6), 209–217. doi:10.1016/S0273-1223(98)00582-4
   Web of Science ®Google Scholar
• Martins, A., Leitão, J.P., & Amado, C. (2013). Comparative study of three stochastic models for prediction of pipe failures in water supply systems. Journal of Infrastructure Systems, 19(4), 442–450. doi:10.1061/(ASCE)IS.1943-555X.0000154
   Web of Science ®Google Scholar
• MathWorks (2016). Statistics and machine learning Toolbox 2016b. Natick, MA: The MathWorks Inc.
   Google Scholar
• McKinney, W., & Team, P.D. (2015). Pandas – Powerful Python data analysis toolkit. Pandas –Powerful Python Data Analysis Toolkit, 1625.
   Google Scholar
• Mitchell, T. (1997). Machine learning. McGraw Hill Series in Computer Science. Retrieved from https://profs.info.uaic.ro/~ciortuz/SLIDES/ml0.pdf
   Google Scholar
• Mounce, S.R., Ellis, K., Edwards, J.M., Speight, V.L., Jakomis, N., & Boxall, J.B. (2017). Ensemble decision tree models using rusboost for estimating risk of iron failure in drinking water distribution systems. Water Resources Management, 31(5), 1575–1589. doi:10.1007/s11269-017-1595-8
   Web of Science ®Google Scholar
• Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., Chan-Hilton, A., Karamouz, M., Minsker, B., Ostfeld, A., Singh, A., Zechman, E. (2010). State of the art for genetic algorithms and beyond in water resources planning and management. Journal of Water Resources Planning and Management, 136(4), 412–432. doi:10.1061/(ASCE)WR.1943-5452.0000053
   Web of Science ®Google Scholar
• Osman, H., & Bainbridge, K. (2011). Comparison of statistical deterioration models for water distribution networks. Journal of Performance of Constructed Facilities, 25(3), 259–266. doi:10.1061/(ASCE)CF.1943-5509
   Web of Science ®Google Scholar
• Puz, G., & Radic, J. (2011). Life-cycle performance model based on homogeneous Markov processes. Structure and Infrastructure Engineering, 7(4), 285–296. doi:10.1080/15732470802532943
   Web of Science ®Google Scholar
• Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. doi:10.1023/A:1022643204877
   Google Scholar
• Rokach, L., & Maimom, O. (2014). Data mining with decision trees: Theory and applications (2nd ed.). Singapore: World Scientific Pte. Ltd. doi:10.1007/978-0-387-09823-4
   Google Scholar
• Rokstad, M.M., & Ugarelli, R.M. (2015). Evaluating the role of deterioration models for condition assessment of sewers. Journal of Hydroinformatics, 17(5), 789–804.10.2166/hydro.2015.122
   Web of Science ®Google Scholar
• Roscher, H. (2000). Zustandsbewertung städtischer Wasserrohrleitungen zur Vorbereitung der Rehabilitation [Condition assessment of urban water distribution pipes to precede rehabilitation]. ROHRBAU-Kongress 2000 in Weimar. FITR - Forschungsinstitut für Tief- und Rohrleitungsbau Weimar e.V, Ed, Weimar, Germany.
   Google Scholar
• Sægrov, S. (2005). Care-W computer aided rehabilitation of water networks, 1843390914. London: IWA.
   Google Scholar
• Salehi, S., Jalili Ghazizadeh, M., & Tabesh, M. (2017). A comprehensive criteria-based multi-attribute decision-making model for rehabilitation of water distribution systems. Structure and Infrastructure Engineering, 6, 1–23. doi:10.1080/15732479.2017.1359633
   Google Scholar
• Santos, P., Amado, C., Coelho, S.T., & Leitão, J.P. (2017). Stochastic data mining tools for pipe blockage failure prediction. Urban Water Journal, 14(4), 343–353. doi:10.1080/1573062X.2016.1148178
   Web of Science ®Google Scholar
• Scheidegger, A., Leitão, J.P., & Scholten, L. (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
• Scholten, L., Scheidegger, A., Reichert, P., & Maurer, M. (2013). Combining expert knowledge and local data for improved service life modeling of water supply networks. Environmental Modelling & Software, 42, 1–16. doi:10.1016/j.envsoft.2012.11.013
   Web of Science ®Google Scholar
• Seiffert, C., Khoshgoftaar, T.M., Van Hulse, J., & Napolitano, A. (2010). RUSBoost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, 40(1), 185–197. doi:10.1109/TSMCA.2009.2029559
   Web of Science ®Google Scholar
• Shahata, K., & Zayed, T. (2012). Data acquisition and analysis for water main rehabilitation techniques. Structure and Infrastructure Engineering, 8(11), 1054–1066. doi:10.1080/15732479.2010.502179
   Web of Science ®Google Scholar
• Sorge, H.-C. (2006). Technische Zustandsbewertung metallischer Wasserversorgungsleitungen als Beitrag zur Rehabilitationsplanung [Technical condition assessment of metallic water distribution pipes as contribution to rehabilitation planning]. Weimar, Germany: Bauhaus-Universität Weimar.
   Google Scholar
• Syachrani, S., Jeong, H.S., & Chung, C.S. (2013). Decision tree-based deterioration model for buried wastewater pipelines. Journal of Performance of Constructed Facilities, 27(5), 633–645. doi:10.1061/(ASCE)CF.1943-5509.0000349
   Web of Science ®Google Scholar
• Tran, D.H., Ng, A.W.M., & Perera, B.J.C. (2007). Neural networks deterioration models for serviceability condition of buried stormwater pipes. Engineering Applications of Artificial Intelligence, 20(8), 1144–1151. doi:10.1016/j.engappai.2007.02.005
   Web of Science ®Google Scholar
• Tscheikner-Gratl, F. (2016). Integrated approach for multi-utility rehabilitation planning of urban water infrastructure. PhD Thesis, Innsbruck University Press.
   Google Scholar
• Tscheikner-Gratl, F., Sitzenfrei, R., Hammerer, M., Rauch, W., & Kleidorfer, M. (2014). Prioritization of rehabilitation areas for urban water infrastructure. A case study. Procedia Engineering, 89, 811–816. doi:10.1016/j.proeng.2014.11.511
   Google Scholar
• Tscheikner-Gratl, F., Sitzenfrei, R., Rauch, W., & Kleidorfer, M. (2016). Enhancement of limited water supply network data for deterioration modelling and determination of rehabilitation rate. Structure and Infrastructure Engineering, 12(3), 366–380. doi:10.1080/15732479.2015.1017730
   Web of Science ®Google Scholar
• Wilson, D., Filion, Y., & 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. doi:10.1080/1573062X.2015.1080848
   Web of Science ®Google Scholar