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Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes

  • Water Engineering
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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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Ab Ghani, A. (1993). Sediment transport in sewers, PhD Thesis, University of Newcastle upon Tyne, UK.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Draper, N. R. and Smith, H. (1998). Applied regression analysis, Wiley & Sons, New York.

    Book  MATH  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd edition), Prentice-Hall Inc., Englewood Cliffs, New Jersey, USA.

    MATH  Google Scholar 

  • 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).

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Quinlan, J. R. (1992). “Learning with continuous classes.” Adams, Sterling, editors. Proceedings of AI’92. World Scientific, pp. 343–348.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

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Correspondence to Mohammad Najafzadeh.

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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

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