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Use of Evolutionary Polynomial Regression (EPR) for Prediction of Total Sediment Load of Malaysian Rivers
Nadiatul Adilah Ahmad Abdul Ghani, Mohamed A. Shahin, Hamid R. Nikraz
Pages - 262 - 277     |    Revised - 15-09-2012     |    Published - 24-10-2012
Volume - 6   Issue - 5    |    Publication Date - October 2012  Table of Contents
Sediment, River, Modelling
This study presents the use of Evolutionary Polynomial Regression (EPR) in predicting the total sediment load of ten selected rivers in Malaysia. EPR is a data-driven hybrid technique, based on evolutionary computing. In order to apply the method, the extensive database of the Department of Irrigation and Drainage (DID), Ministry of Natural Resources & Environment, Malaysia was sought, and unrestricted access was granted. The EPR technique produced greatly improved results compared to other previous sediment load methods. A robustness study was performed in order to confirm the generalisation ability of the developed EPR model, and a sensitivity analysis was also conducted to determine the relative importance of model inputs. The performance of the EPR model demonstrates its predictive capability and generalisation ability to solve highly nonlinear problems of river engineering applications, such as sediment.
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2 Jaiyeola, A. T., & Bwapawa, J. K. (2015).Dynamics of sedimentation and use of genetic algorithms for estimating sediment yields in a river: a critical review. natural resource modeling.
3 Shahnazari, H., Shahin, M. A., & Tutunchian, M. A. (2014). Evolutionary-based approaches for settlement prediction of shallow foundations on cohesionless soils. Geotech Eng, 12(1), 55-64.
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Ab. Ghani, A., Azamathulla, H.Md., Chang, C.K., Zakaria, N.A., Hassan, Z.A. “ Prediction of total material load for rivers in Malaysia: A case study of Langat, Muda and Kurau Rivers.” Environ Fluid Mech, vol. 11, pp. 307-318, 2011.
Ackers P. and White W.R. (1973) “Sediment transport: new approach and analysis.”Journal of the Hydraulics Division. ASCE, vol. 99(11), pp. 2041-2060, 1973.
Ariffin J. “Development of sediment transport models for rivers in Malaysia using regression analysis and artificial neural networks.” PhD Thesis, Universiti Sains Malaysia,Malaysia, 2004.
Azamathulla, H. Md., Chang, C.K., Ab. Ghani. A., Ariffin, J., Zakaria, N.A. and Abu Hassan, Z. “An ANFIS-based approach for predicting the bed load for moderately sized rivers.” Journal of Hydro-environmental Research”, vol. 3, pp. 35-44, 2009.
Berardi L., Giustolisi O., Kapelan Z. and Savic, D.A. “Development of pipe deterioration models for water distribution systems using EPR.” Journal of Hydro Informatics, vol. 10(2), pp. 113-126, 2008.
Chan C.K., Ab. Ghani, A., Zakaria N.A., Abu Hasan Z. and Abdullah R. “Sediment transport equation assessment for selected rivers in Malaysia.” International Journal of River Basin Management, vol. 3(3), pp. 203-208, 2005.
Cortez P., Cerdeira A., Almeida F., Matos T., and Reis J. “Modeling wine preferences by data mining from physicochemical properties.” Decision Support Systems, vol. 47(4), pp.547-553, 2009.
Draper N.R., and Smith H. Applied regression analysis. New York: John Wiley and Sons,1998.
Engelund F. and Hansen. A monograph on sediment transport in alluvial streams.Denmark: Copenhagen. Teknisk Forlag, 1967.
Giustolisi O., Doglioni A., Savic D.A. and Pierro F. “An evolutionary multiobjective strategy for the effective management of groundwater resources.” Water Resources Research Journal, vol. 44(W01403), pp. 1-14, 2008.
Giustolisi O., Doglioni A., Savic D.A. and Webb, B.W. “A multi-model approach to analysis of environmental phenomena.” Environmental Modelling & Software Journal. vol,22 pp. 674-682, 2007.
Giustolisi, O. and Savic D.A. “A novel strategy to perform genetic programming:Evolutionary Polynomial Regression. “Sixth International Conference on Hydroinformatics, Singapore, 2004, pp. 787-794.
Giustolisi, O. and Savic D.A. “A symbolic data driven technique based on Evolutionary Polynomial Regression.” Journal of Hydroinformatics, vol. 8(3), pp. 207-222, 2006.
Giustolisi, O., Savic, D.A., “Evolutionary Polynomial Regression (EPR): Development and Application.” Report 2003/1. School of Engineering, Computer Science and Mathemathics, Centre for Water Systems, University of Exeter, 2003.
Goldberg D.E. Genetic algorithms in search, optimization and machine learning,Massachussets: Addison Wesley, 1989.
Graf W.H. Hydraulics of sediment transport. New York: McGraw Hill, 1971.
Karim F. “Bed material discharge prediction for non-uniform bed sediments.” Journal of Hydraulic Engineering, ASCE, vol. 124(6), pp. 597-604, 1998.
Kewley R., Embrechts M. and Breneman C.”Data strip mining for the virtual design of pharmaceuticals with neural networks.” IEEE Trans Neural Networks, vol. 11(3), pp. 668-679, 2000.
Koza J.R. Genetic programming: on the programming of computers by means of natural selection. MIT Press, Massachusetts, 1992.
Laucelli D., Berardi L. and Dogliono A. Evolutionary polynomial regression (EPR) - toolbox, Version 2.0 SA, Department of Civil and Environmental Engineering, Technical University of Bari, Italy, 2009.
Legates D.R. and McCabe Jr. G.J. “Evaluating the use of “Goodness-of-Fit” measures in hydrologic and hydroclimatic model validation.” Water Resources Research, vol. 35(1),pp. 233-241, 1999.
Nagy H.M., Watanabe K. and Hirano M. “Prediction of sediment load concentration in rivers using artificial neural network model.” Journal of Hydraulic Engineering, ASCE, vol.128(6), pp. 558-595, 2002.
Rezania M., Faramarzi A. and Javadi A. “An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement.”Engineering Applications of Artificial Intelligence, vol. 24(1), pp. 142-153, 2011.
Savic D.A., Giutolisi O., Berardi L., Shepherd W., Djordjevic S. and Saul A. “Modelling sewer failure by evolutionary computing.” Proceeding of the Institution of Civil Engineers,Water Management, vol. 159(2), pp. 111-118, 2006.
Shahin M.A., Maier H.R. and Jaksa M.B. “Data division for developing neural networks applied to geotechnical engineering.” Journal of Computing in Civil Engineering, ASCE,vol. 18(2), pp.105-114, 2004.
Shahin M.A., Maier H.R. and Jaksa M.B. “Investigation into the robustness of artificial neural networks for a case study in civil engineering.” International Congress on Modelling and Simulation: Melbourne, 2004.
Shaqlaih A., White L. and Zaman M. “Resilient modulus modeling with information theory approach.” International Journal of Geomechanics, in press.
Sinnakaudan S.K., Ab.Ghani A., Ahmad M.S. and Zakaria N.A. “Multiple linear regression model for total bed material load prediction.” Journal of Hydraulic Engineering, ASCE,vol. 132(5), pp. 521-528, 2006.
Van Rijn L.C. “Mathematical modelling of suspended sediment in non-uniform flows.”Journal of Hydraulic Engineering, ASCE, vol. 112(6), pp. 433-455, 1986.
Watson A., Parmee I. “System identification using genetic programming” Proceedings of ACEDC’96, University of Plymouth, United Kingdom, 1996.
Yang C.T and Molinas A. “ Sediment transport and unit stream power function”, Journal of Hydraulic Engineering, ASCE, vol. 108(6), pp. 774-793, 1982.
Zakaria N.A, Azamathulla H.Md, Chang C.K. and Ab. Ghani A. “Gene expression programming for total bed material load estimation-a case study.” Journal of Science of the Total Environment, vol. 408(21), pp. 5078-5085, 2010.
Miss Nadiatul Adilah Ahmad Abdul Ghani
Associate Professor Mohamed A. Shahin
Professor Hamid R. Nikraz

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