Abstract
Prediction of critical velocity for sediment deposition is a significant component in design of sewer pipes. Because of the abrupt changes in velocity and shear stress distributions, traditional equations based on regression analysis can fail in evaluating sediment transport efficiently. Therefore, different artificial intelligence approaches have been applied to investigate sediment transport in sewer pipes. This study proposes two different approaches to predict the critical velocity for sediment deposition in sewer networks: Model Tree (MT) and the Evolutionary Polynomial Regression (EPR), a hybrid data-driven technique that combines genetic algorithms with numerical regression. The hydraulic radius, average size of sediments, volumetric concentration, total friction factor, and non-dimensional sediment size were considered as input parameters to characterize sediment transport in clean sewer pipes. The present study implements data collected from different works in literature. The proposed modeling approaches are compared to some benchmark formulas from literature, and discussed from the accuracy and knowledge discovery points of view, highlighting the advantage of both proposed techniques. Results indicated that both techniques have similar accuracy in predictions, but EPR allows to physical validation of returned formulas, allowing identifying the most influent inputs on the phenomenon at stake.
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References
American Society of Civil Engineers (ASCE). “Water pollution control federation, design and construction of sanitary and storm sewers.” American Society of Civil Engineers Manuals and Reports on Engineering Practices, No. 37, 1970.
Arthur, S., Ashley, R., Tait, S., and Nalluri, C. (1999). “Sediment transport in sewers-a step towards the design of sewers to control sediment problems.” P. I. Civil Eng.Water, Vol.136, No. 1, pp. 9–19, DOI: 10.1680/iwtme.1999.31264.
Ab Ghani, A. (1993). Sediment transport in sewers, PhD Thesis, University of Newcastle upon Tyne, UK.
Ab Ghani, A. and Azamathulla, H. Md (2011). “Gene-expression programming for sediment transport sewer pipe systems.” J. Pipeline. Syst. Eng. Prac., ASCE. Vol. 2, No. 3, pp. 102–106, DOI: 10.1061/(ASCE)PS.1949-1204.0000076.
Azamathulla, H. Md., Ghani, A. A., and Seow, Y. F. (2012). “ANFISbased approach for predicting sediment transport in clean sewer.” Applied Soft Comput., Vol. 12, No. 3, pp. 1227–1230, DOI: 10.1016/j.asoc.2011.12.003.
Bhattacharya, B. and Solomatine, D. P. (2005). “Neural networks and M5 model trees in modelling water level-discharge relationship.” Neurocomp., Vol. 63, pp. 381–396, DOI: 10.1016/j.neucom.2004.04.016.
Doglioni, A., Mancarella, D., Simeone, V., and Giustolisi, O. (2010) “Inferring groundwater system dynamics from hydrological timeseries data.” Hydrol. Sci. J., Vol. 55, No. 4, pp. 593–608, DOI: 10.1080/02626661003747556.
Draper, N. R. and Smith, H. (1998). Applied regression analysis, Wiley & Sons, New York.
Ebtehaj, I. and Bonakdari, H. (2013). “Evaluation of Sediment Transport in Sewer Using Artificial Neural Network.” Eng. Appl. Comput. Fluid Dynamics., Vol. 7, No. 3, pp. 382–392, DOI: 10.1080/19942060.2013.11015479.
Ebtehaj, I. and Bonakdari, H. (2014). “Performance evaluation of adaptive neural fuzzy inference system for sediment transport in sewers.” Water Resources Management, Vol. 28, No. 13, pp. 4765–4779.
Etemad-Shahidi, A. and Ghaemi, N. (2011). “Model tree approach for prediction of pile groups scour due to waves.” Ocean Engineering, Vol. 38, pp. 1522–1527, DOI: 10.1016/j.oceaneng.2011.07.012.
Faramarzi, A., Alani, A. M., and Javadi, A. A. (2014) “An EPR-based self-learning approach to material modelling.” Computers & Structures, Vol. 137, pp. 63–71, DOI: 10.1016/j.compstruc.2013.06.012.
Giustolisi, O. and Savic, D. A. (2006). “A symbolic data-driven technique based on evolutionary polynomial regression.” J. of Hydroinformatics, Vol. 8, pp. 207–222, DOI: 10.2166/hydro.2006.020.
Giustolisi, O., Doglioni, A., Savic, D. A., and Webb, B. (2007). “A multi-model approach to analysis of environmental phenomena.” Environmental Modelling & Software, Vol. 22, pp. 674–682, DOI: 10.1016/j.envsoft.2005.12.026.
Giustolisi, O. and Savic, D. A. (2009). “Advances in data-driven analyses and modelling using EPR-MOGA.” J. of Hydroinformatics, Vol. 11, pp. 225–236, DOI: 10.2166/hydro.2009.017.
Goyal, M. K. (2014). “Modeling of sediment yield prediction using m5 model tree algorithm and wavelet regression.” Water Resources Management, Vol. 28, No. 7, pp. 1991–2003, DOI: 10.1007/s11269-014-0590-6.
Ghaemi, N., Etemad-Shahidi, A., and Ataie-Ashtiani, B. (2013). “Estimation of current-induced pile groups scour using a rule based method.” Journal of Hydroinformatics, IWA, Vol. 15, pp. 516–528, DOI: 10.2166/hydro.2012.175.
Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd edition), Prentice-Hall Inc., Englewood Cliffs, New Jersey, USA.
Laucelli, D., Berardi, L., Doglioni, A., and Giustolisi, O. (2012). “EPRMOGA-XL: An excel based paradigm to enhance transfer of research achievements on data-driven modeling.” Proceedings of 10th International Conference on Hydroinformatics HIC 2012, 14-18 July, Hamburg, Germany, R. Hinkelmann, M.H. Nasermoaddeli, S.Y. Li-ong, D. Savic, P. Fröhle (Eds).
Laucelli, D. and Giustolisi, O. (2011). “Scour depth modelling by a multi-objective evolutionary paradigm.” Environmental Modeling & Software, Vol. 26, No. 4, pp. 498–509, DOI: 10.1016/j.envsoft.2010.10.013.
Mayerle, R., Nalluri, C., and Novak, P. (1991). “Sediment transport in rigid bed conveyances.” J. Hydraul. Res., Vol. 29, No. 4, pp. 475–496, DOI: 10.1080/00221689109498969.
Nalluri, C., Ghani, A. A., and El-Zaemey, A. K. S. (1994). “Sediment transport over deposited beds in sewers.” Water Sci. Tech., Vol. 29, pp. 125–133.
Nalluri, C. and Ab. Ghani, A. (1996). “Design options for self-cleansing storm sewers.” Water Sci. Tech., Vol. 33, No. 9, pp. 215–220, http://www.iwaponline.com/wst/03309/wst033090215.htm.
Novak, P. and Nalluri, C. (1975). “Sediment transport in smooth fixed bed channels.” J. Hydraul. Div., ASCE. Vol. 101, No. 9, pp. 1139–1154, http://cedb.asce.org/cgi/WWWdisplay.cgi?6183.
Quinlan, J. R. (1992). “Learning with continuous classes.” Adams, Sterling, editors. Proceedings of AI’92. World Scientific, pp. 343–348.
Rezania, M., Javadi, A. A., and Giustolisi, O. (2010). “Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression.” Computers and Geotechnics, Vol. 37, pp. 82–92, DOI: 10.1162/106365600568158.
Savic, D. A., Giustolisi, O., Berardi, L., Shepherd, W., Djordjevic, S., and Saul, A. (2006). “Modelling sewer failure by evolutionary computing.” Water Management Journal, Vol. 159, No. 2, pp. 111–118, DOI: 10.1680/wama.2006.159.2.111.
Solomatine, D. P. and Xue, Y. P. (2004). “M5 model trees and neural networks: Application to flood forecasting in the upper reach of the Huai River in China.” J. Hydrol. Eng., Vol. 9, No. 6, pp. 491–501, DOI: 10.1061/(ASCE)1084-0699(2004)9:6(491).
Singh, K. K., Pal, M., and Singh, V. P. (2009). “Estimation of mean annual flood in indian catchments using backpropagation neural network and M5 model tree.” Water Resources Management, Vol. 24, No. 10, pp. 2007–2019, DOI: 10.1007/s11269-009-9535-x.
Wang, Y. and Witten, I. H. (1997). “Induction of model trees for predicting continuous lasses.” Proceedings of the Poster Papers of the European Conference on Machine Learning, University of Economics, Faculty of Informatics and Statistics, Prague.
Vongvisessomjai, N., Tingsanchali, T., and Babel, M. S. (2010). “Nondeposition design criteria for sewers with part-full flow.” Urban Water J., Vol. 7, No. 1, pp. 61–77, DOI: 10.1080/15730620903242824.
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Najafzadeh, M., Laucelli, D.B. & Zahiri, A. Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes. KSCE J Civ Eng 21, 1956–1963 (2017). https://doi.org/10.1007/s12205-016-1784-7
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DOI: https://doi.org/10.1007/s12205-016-1784-7