A comparison between multivariate adaptive regression splines and regressive convolution neural network with support vector regression for pipe burst rate prediction on limited dataset

References

• Achim, D., F. Ghotb, and K. J. McManus. 2007. “Prediction of Water Pipe Asset Life Using Neural Networks.” Journal of Infrastructure Systems 13 (1): 26–30. doi:10.1061/(ASCE)1076-0342(2007).
   Web of Science ®Google Scholar
• Alexopoulos, E. C. 2010. “Introduction to Multivariate Regression Analysis.” Hippokratia 14: 23–28.
   PubMed Web of Science ®Google Scholar
• Alizadeh, Z., J. Yazdi, S. Mohammadiun, K. Hewage, R. Sadiq, et al. 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
• Amiri-Ardakani, Yaser, and Mohammad Najafzadeh. 2021. “Pipe Break Rate Assessment while considering Physical and Operational Factors: A Methodology Based on Global Positioning System and data-driven Techniques.” Water Resources Management 35 (11): 3703–3720. doi:10.1007/s11269-021-02911-6.
   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): 113–126. doi:10.2166/hydro.2008.012.
   Web of Science ®Google Scholar
• Boryczko, Krzysztof, I. Piegdoń, D. Szpak, J. Żywiec, et al. 2021. “Risk Assessment of Lack of Water Supply Using the Hydraulic Model of the Water Supply.” Resources 10 (5): 43. doi:10.3390/resources10050043.
   Google Scholar
• Brett, Snider, and Edward A. McBean. 2020. “Improving Urban Water Security through pipe-break Prediction Models: Machine Learning or Survival Analysis.” Journal of Environmental Engineering 146 (3): 04019129. doi:10.1061/(ASCE)EE.1943-7870.0001657.
   Web of Science ®Google Scholar
• Christodoulou, S., P. Aslani, and A Vanreterghem. 2004. “A Risk Analysis Framework for Evaluating Structural Degradation of Water Mains in Urban Settings, Using Neurofuzzy Systems and Statistical Modeling Techniques.” In Proceedings of the World Water and Environmental Resources Congress and Related Symposia. Philadelphia, PA: ASCE.
   Google Scholar
• Deo, R. C., O. Kisi, and V. P. Singh. 2017. “Drought Forecasting in Eastern Australia Using Multivariate Adaptive Regression Spline, Least Square Support Vector Machine and M5Tree Model.” Atmospheric Research 184: 149–175. doi:10.1016/j.atmosres.2016.10.004.
   Web of Science ®Google Scholar
• Emamgholizadeh, S., S. Bateni, D. Shahsavani, T. Ashrafi, and H. Ghorbani. 2015. “Estimation of Soil Cation Exchange Capacity Using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS).” Journal of Hydrology 529. doi:10.1016/j.jhydrol.2015.08.025.
   Web of Science ®Google Scholar
• Farmani, Raziyeh, K. Kakoudakis, K. Behzadian, D. Butler, et al. 2017. “Pipe Failure Prediction in Water Distribution Systems considering Static and Dynamic Factors”. Procedia Engineering 186: 117–126. doi:10.1016/j.proeng.2017.03.217.
   Google Scholar
• Forghani, A., and R. Peralta. 2017. “Transport Modeling and Multivariate Adaptive Regression Splines for Evaluating Performance of ASR Systems in Freshwater Aquifers.” Journal of Hydrology 553: 540–548. doi:10.1016/j.jhydrol.2017.08.012.
   Web of Science ®Google Scholar
• Friedman, J. H. 1991. “Multivariate Adaptive Regression Splines.” Annals of Statistics 19 (1): 1–67. doi:10.1214/aos/1176347963.
   Web of Science ®Google Scholar
• Grigg, N. 2007. Main Break Prediction, Prevention and Control. Denver, CO: American Water Works Research Foundation.
   Google Scholar
• Heddam, S., and O Kisi. 2018. “Modelling Daily Dissolved Oxygen Concentration Using Least Square Support Vector Machine,multivariate Adaptive Regression Splines and M5 Model Tree.” Journal of Hydrology 559: 499–509. doi:10.1016/j.jhydrol.2018.02.061.
   Web of Science ®Google Scholar
• Kakoudakis, K. 2019. “Pipe Failure Prediction and Impacts Assessment in a Water Distribution Network, ac Thesis for the Degree of Doctor of Philosophy in Engineering.” University of Exeter, March.
   Google Scholar
• Kleiner, Y., and B. Rajani. 2010. “I-warp: Individual Water Main Renewal Planner.” Drinking Water Engineering and Science 3 (1): 71–77. doi:10.5194/dwes-3-71-2010.
   Google Scholar
• Konstantinou, Charalampos, and Stoianov Ivan. 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
• Kutylowska, M. 2017. “Regression Methods for Predicting Rate and Type of Failures of Water Conduits.” Ecological Chemistry and Engineering A 24 (2): 193–205.
   Web of Science ®Google Scholar
• Kutylowska, M. 2018. “Forecasting Failure Rate of Water Pipes.” Water Supply 19 (1): 264–273. doi:10.2166/ws.2018.078.
   Google Scholar
• Kutylowska, M. 2019. “Application of MARSplines Method for Failure Rate Prediction.” Periodica Polytechnica Civil Engineering 63 (1): 87–92.
   Web of Science ®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
• Rajani, B., and S. Tesfamariam. 2007. “Estimating Time to Failure of Cast Iron Water Mains.” Proceedings of the ICE - Water Management 160 (2): 83–88.
   Web of Science ®Google Scholar
• Randall-Smith, M., A. Russell, and R. Oliphant. 1992. Guidance Manual for the Structural Condition Assessment of the Trunk Mains. Swindon, UK: Water Research Centre.
   Google Scholar
• Rezaie-Balf, M. 2019. “Multivariate Adaptive Regression Splines Model for Prediction of Local Scour Depth Downstream of an Apron under 2D Horizontal Jets.” Iranian Journal of Science and Technology - Transactions of Civil Engineering 43 (1): 103–115. doi:10.1007/s40996-018-0151-y.
   Google Scholar
• Safari, M. J. S. 2019. “Decision Tree (DT), Generalized Regression Neural Network (GR) and Multivariate Adaptive Regression Splines (MARS) Models for Sediment Transport in Sewer Pipes.” Water Science & Technology 79: 1113–1122. doi:10.2166/wst.2019.106.
   PubMed Web of Science ®Google Scholar
• Sharda, V., S. Prasher, R. Patel, P. Ojasvi, and C. Prakash. 2008. “Performance of Multivariate Adaptive Regression Splines (MARS) in Predicting Runoff in mid-Himalayan micro-watersheds with Limited data/Performances de Régressions Par Splines Multiples Et Adaptives (MARS) Pour la Prévision D’écoulement Au Sein de micro-bassins Versants Himalayens D’altitudes Intermédiaires Avec Peu de Données.” Hydrological Sciences Journal 53 (6): 1165–1175.
   Web of Science ®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
• Vapnik, V. 1995. The Nature of Statistical Learning Theory. New-York: Springer-Verlag.
   Google Scholar
• Vapnik, V. 1998a. Statistical Learning Theory. New-York: John Wiley.
   Google Scholar
• Vapnik, V. 1998b. “The Support Vector Method of Function Estimation.“ In Suykens, Johan A.K, Vandewalle, Joos P.L. Nonlinear Modeling: Advanced black-box Techniques, 55–85. Boston: Kluwer Academic Publishers.
   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
• Zhang, Youshan, and Qi Li. 2019. “A Regressive Convolution Neural Network and Support Vector Regression Model for Electricity Consumption Forecasting.” In Future of Information and Communication Conference. Springer, Cham.
   Google Scholar
• Zhou, Xiao, Z. Tang, W. Xu, F. Meng, X. Chu, K. Xin, G. Fu, et al. 2019. “Deep Learning Identifies Accurate Burst Locations in Water Distribution Networks”. Water Research 166: 115058. doi:10.1016/j.watres.2019.115058.
   PubMed Web of Science ®Google Scholar