Technical Papers
Dec 8, 2020

Model Development for Estimation of Sediment Removal Efficiency of Settling Basins Using Group Methods of Data Handling

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Publication: Journal of Irrigation and Drainage Engineering
Volume 147, Issue 2

Abstract

Settling basins are generally used as sediment removal structures in which flow velocity is reduced, resulting in surplus settlement of sediment particles. The accuracy of the available empirical equations for sediment removal efficiency is checked using data available in the literature. The existing relationships of removal efficiency were not found to yield satisfactory results. Therefore, the data were reanalyzed and new models were developed using nonlinear regression and the group method of data handling (GMDH). On the basis of various performance parameters, it was observed that the proposed nonlinear regression model had the highest accuracy compared with other available relationships. It was also observed that the efficiency estimated using the GMDH model (R=0.960, AAD=14.698, and RMSE=0.094) was more accurate than that estimated by the regression model (R=0.863, AAD=30.675, and RMSE=0.171). Sensitivity analysis indicated that the ratio of particle fall velocity to average flow velocity in the basin was the most effective parameter for sediment removal efficiency. This work will help in quantifying and subsequently improving the management of surplus sediment in canals.

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Data Availability Statement

The data generated during the study are available from the authors by request.

Acknowledgments

The authors gratefully acknowledge the financial support for this research from the Department of Civil Engineering, Aligarh Muslim University, India.

References

Ansari, M. A., and M. Athar. 2013. “Artificial neural networks approach for estimation of sediment removal efficiency of vortex settling basins.” ISH J. Hydraul. Eng. 19 (1): 38–48. https://doi.org/10.1080/09715010.2012.758415.
Azmathullah, H. M., M. C. Deo, and P. B. Deolalikar. 2005. “Neural networks for estimation of scour downstream of a ski-jump bucket.” J. Hydraul. Eng. 131 (10): 898–908. https://doi.org/10.1061/(ASCE)0733-9429(2005)131:10(898).
Camp, T. R. 1946. “Sedimentation and the design of settling tanks.” Trans. ASCE 111 (1): 895–958.
Dey, S. 1999. “Sediment threshold.” Appl. Math. Modell. 23 (5): 399–417. https://doi.org/10.1016/S0307-904X(98)10081-1.
Dey, S. 2003. “Threshold of sediment motion on combined transverse and longitudinal sloping beds.” J. Hydraul. Res. 41 (4): 405–415. https://doi.org/10.1080/00221680309499985.
Faisal, A., A. Mujib, H. Ajmal, and J. Jahangeer. 2020. “Model development for estimation of sediment removal efficiency of settling basins using group method of data handling.” Authorea. https://doi.org/10.22541/au.158169830.00283971.
Garde, R. J., K. G. Ranga Raju, and A. W. R. Sujudi. 1990. “Design of settling basins.” J. Hydraul. Res. 28 (1): 81–91. https://doi.org/10.1080/00221689009499148.
Garg, V. 2015. “Inductive group method of data handling neural network approach to model basin sediment yield.” J. Hydrol. Eng. 20 (6): C6014002. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001085.
Hashid, M., A. Hussain, and Z. Ahmad. 2015. “Discharge characteristics of lateral circular intakes in open channel flow.” Flow Meas. Instrum. 46 (Part A): 87–92. https://doi.org/10.1016/j.flowmeasinst.2015.10.005.
Himanshu, S. K., A. Pandey, and P. Shrestha. 2017. “Application of SWAT in an Indian river basin for modeling runoff, sediment and water balance.” Environ. Earth Sci. 76 (1): 3. https://doi.org/10.1007/s12665-016-6316-8.
Hussain, A., Z. Ahmad, and G. L. Asawa. 2010. “Discharge characteristics of sharp-crested circular side orifices in open channels.” Flow Meas. Instrum. 21 (3): 418–424. https://doi.org/10.1016/j.flowmeasinst.2010.06.005.
Hussain, S., A. Hussain, and Z. Ahmad. 2014. “Discharge characteristics of orifice spillway under oblique approach flow.” Flow Meas. Instrum. 39 (Oct): 9–18. https://doi.org/10.1016/j.flowmeasinst.2014.05.022.
Ivakhnenko, A. G., and G. Ivakhnenko. 1995. “The review problems solvable by algorithms of group method of data handling (GMDH).” Pattern Recognit. Image Anal. 5 (4): 527–535.
Jahangeer, P. K. Gupta, and B. K., Yadav. 2017. “Transient water flow and nitrate movement simulation in partially saturated zone.” J. Irrig. Drain. Eng. 143 (12): 04017048. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001238.
Jain, S. K. 2001. “Development of integrated sediment rating curves using ANNs.” J. Hydraul. Eng. 127 (1): 30–37. https://doi.org/10.1061/(ASCE)0733-9429(2001)127:1(30).
Kang, J., S. E. King, and R. A. McLaughlin. 2016. “Flocculated sediments can reduce the size of sediment basin at construction sites.” J. Environ. Manage. 166 (Jan): 450–456. https://doi.org/10.1016/j.jenvman.2015.10.049.
Kerssens, P. J. M., A. Prins, and L. C. Van Rijn. 1979. “Model for suspended sediment transport.” J. Hydraul. Div. 105 (5): 461–476.
Najafzadeh, M., and H. M. Azamathulla. 2012. “Group method of data handling to predict scour depth around bridge piers.” Neural Comput. Appl. 23 (7–8): 2107–2112. https://doi.org/10.1007/s00521-012-1160-6.
Najafzadeh, M., and H. M. Azamathulla. 2015. “Neuro-fuzzy GMDH to predict the scour pile groups due to waves.” J. Comput. Civ. Eng. 29 (5): 04014068. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000376.
Najafzadeh, M., and G. A. Barani. 2011. “Comparison of group method of data handling based genetic programming and back propagation systems to predict scour depth around bridge piers.” Sci. Iranica 18 (6): 1207–1213. https://doi.org/10.1016/j.scient.2011.11.017.
Najafzadeh, M., G.-A. Barani, and H. M. Azamathulla. 2012. “Prediction of pipeline scour depth in clear-water and live-bed conditions using group method of data handling.” Neural. Comput. Appl. 24 (3–4): 629–635. https://doi.org/10.1007/s00521-012-1258-x.
Najafzadeh, M., G.-A. Barani, and M. R. Hessami Kermani. 2013a. “Abutment scour in clear-water and live-bed conditions by GMDH network.” Water Sci. Technol. 67 (5): 1121–1128. https://doi.org/10.2166/wst.2013.670.
Najafzadeh, M., G.-A. Barani, and M. R. Hessami Kermani. 2013b. “GMDH based back propagation algorithm to predict abutment scour in cohesive soils.” Ocean Eng. 59 (Feb): 100–106. https://doi.org/10.1016/j.oceaneng.2012.12.006.
Najafzadeh, M., and A. Sattar. 2015. “Neuro-fuzzy GMDH approach to predict longitudinal dispersion in water networks.” Water Resour. Manage. 29 (7): 2205–2219. https://doi.org/10.1007/s11269-015-0936-8.
Nayak, P. C., and S. K. Jain. 2011. “Modelling runoff and sediment rate using a neuro-fuzzy technique.” Proc. Inst. Civ. Eng. Water Manage. 164 (4): 201–209. https://doi.org/10.1680/wama.900083.
Pandey, A., S. K. Himanshu, S. K. Mishra, and V. P. Singh. 2016. “Physically based soil erosion and sediment yield models revisited.” Catena 147 (Dec): 595–620. https://doi.org/10.1016/j.catena.2016.08.002.
Papanicolaou, A. N., A. Bdour, and E. Wicklein. 2004. “One-dimensional hydrodynamic/sediment transport model applicable to steep mountain streams.” J. Hydraul. Res. 42 (4): 357–375. https://doi.org/10.1080/00221686.2004.9728402.
Papanicolaou, A. T. N., M. Elhakeem, G. Krallis, S. Prakash, and J. Edinger. 2008. “Sediment transport modeling review—Current and future developments.” J. Hydraul. Eng. 134 (1): 1–14. https://doi.org/10.1061/(ASCE)0733-9429(2008)134:1(1).
Raju, K. G. R., U. C. Kothyari, S. Srivastav, and M. Saxena. 1999. “Sediment removal efficiency of settling basins.” J. Irrig. Drain. Eng. 125 (5): 308–314. https://doi.org/10.1061/(ASCE)0733-9437(1999)125:5(308).
Saberi-Movahed, F., M. Najafzadeh, and A. Mehrpooya. 2020. “Receiving more accurate predictions for longitudinal dispersion coefficients in water pipelines: Training group method of data handling using extreme learning machine conceptions.” Water Resour. Manage. 34 (2): 529–561. https://doi.org/10.1007/s11269-019-02463-w.
Saxena, M. 1996. “Effect of flushing on efficiency of settling basins.” M.E. thesis, Dept. of Civil Engineering, Univ. of Roorkee.
Shariq, A., A. Hussain, and M. A. Ansari. 2018. “Lateral flow through the sharp crested side rectangular weirs in open channels.” Flow Meas. Instrum. 59 (Mar): 8–17. https://doi.org/10.1016/j.flowmeasinst.2017.11.007.
Singh, G., and A. Kumar. 2016. “Performance evaluation of desilting basins of small hydropower projects.” ISH J. Hydraul. Eng. 22 (2): 135–141. https://doi.org/10.1080/09715010.2015.1094750.
Singh, K. K. 1987. “Experimental study of settling basins.” M.E. thesis, Dept. of Civil Engineering, Univ. of Roorkee.
Singh, K. K., M. Pal, C. S. P. Ojha, and V. P. Singh. 2008. “Estimation of removal efficiency for settling basin through neural networks and support vector machines.” J. Hydrol. Eng. 13 (3): 146–155. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:3(146).
Srivastava, S. 1997. “Effect of flushing on the efficiency of settling basin.” M.E. thesis, Dept. of Civil Engineering, Univ. of Roorkee.
Sujudi, A. W. R. 1987. “Design of settling basins.” M.E. thesis, Dept. of Hydrology, Univ. of Roorkee.
Sumer, B. M. 1977. “Settlement of solid particles in open-channel flow.” J. Hydraul. Div. 103 (11): 1323–1337.
United States Bureau of Reclamation. 1949. Boulder Caryon project final rep. Part IV: Design and construction of imperial dam and desilting works. Denver: United States Bureau of Reclamation.
Vanoni, V. A. 1975. Sedimentation engineering, manuals and reports on engineering practice. Reston, VA: ASCE.
Vicenç, P., W. Marcin, N. Fatiha, Q. Joseba, and K. Józef. 2007. “A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test.” Eng. Appl. Artif. Intell. 20 (7): 886–897. https://doi.org/10.1016/j.engappai.2006.12.005.
Wiest, L., et al. 2018. “Priority substances in accumulated sediments in a stormwater detention basin from an industrial area.” J. Environ. Pollut. 243 (Part B): 1669–1678. https://doi.org/10.1016/j.envpol.2018.09.138.
Zubair, A. B., M. S. Sadiq, and S. Abdul Rahman. 2013. “GMDH-based networks for intelligent intrusion detection.” Eng. Appl. Artif. Intell. 26 (7): 1731–1740. https://doi.org/10.1016/j.engappai.2013.03.008.

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Go to Journal of Irrigation and Drainage Engineering
Journal of Irrigation and Drainage Engineering
Volume 147Issue 2February 2021

History

Received: Feb 17, 2020
Accepted: Sep 10, 2020
Published online: Dec 8, 2020
Published in print: Feb 1, 2021
Discussion open until: May 8, 2021

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Faisal Ahmad [email protected]
Research Scholar, Dept. of Civil Engineering, Zakir Hussain College of Engineering and Technology, Aligarh Muslim Univ., Aligarh 202002, India. Email: [email protected]
Mujib Ahmad Ansari [email protected]
Professor, Dept. of Civil Engineering, Zakir Hussain College of Engineering and Technology, Aligarh Muslim Univ., Aligarh 202002, India. Email: [email protected]
Ajmal Hussain [email protected]
Assistant Professor, Dept. of Civil Engineering, Zakir Hussain College of Engineering and Technology, Aligarh Muslim Univ., Aligarh 202002, India. Email: [email protected]
Jahangeer Jahangeer, A.M.ASCE [email protected]
Postdoctoral Research Associate, Nebraska Water Center, Univ. of Nebraska Lincoln, Lincoln, NE 68588 (corresponding author). Email: [email protected]

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