Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques

Rami Raad Ahmed Al-Ani, Basim Hussein Khudair Al-Obaidi


Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers proposed an alternative method for sewer sediment accumulation calculation using predictive models harnessing multiple linear regression model (MLRM) and artificial neural network (ANN). AL-Thawra trunk sewer in Baghdad city is selected as a case study area; data from a survey done on this trunk is used in the modeling process. Results showed that MLRM is acceptable, with an adjusted coefficient of determination (adj. R2) in order of 89.55%. ANN model found to be practical with R2 of 82.3% and fit the data better throughout its range. Sensitivity analysis showed that the flow is the most influential parameter on the depth of sediment deposition.


Sediment Accumulation Model; Trunk Sewers; Neural Network; Regression.


Rammal, M., Chebbo, G., Vazquez, J., & Joannis, C. “Do storm event samples bias the comparison between sewer deposits contribution?” Water Science and Technology 75 (2017): 271–280. doi:10.2166/wst.2016.514.

Fan, C., Field, R., Lai, F. “Sewer-Sediment Control: Overview of an Environmental Protection Agency Wet-Weather Flow Research Program” Journal of Hydraulic Engineering 129 (2003): 253–259. doi:10.1061/(ASCE)0733-9429(2003)129:4(253).

Banasiak R. “Hydraulic performance of sewer pipes with deposited sediments.” Water Science and Technology 57 (June 2008): 1743-1748. doi:10.2166/wst.2008.287.

Hannouche, A., Chebbo, G., Joannis, C “Assessment of the contribution of sewer deposits to suspended solids loads in combined sewer systems during rain events” Environmental Science and Pollution Research 21 (2014): 5311–5317. doi:10.1007/s11356-013-2395-1.

CRABTREE, R. “Sediments in Sewers.” Water and Environment Journal 3 (1989):569–578. doi:10.1111/j.1747-6593.1989.tb01437.x.

Fenner, R.“Approaches to sewer maintenance: a review.” Urban Water 2 (2000):343–356. doi:10.1016/S1462-0758(00)00065-0.

Plihal, H., Kretschmer, F., Bin Ali, M. T., See, C. H., Romanova, A., Horoshenkov, K. V, & Ertl, T. “A novel method for rapid inspection of sewer networks: combining acoustic and optical means.” Urban Water Journal 13(2016):3–14. doi: 10.1080/1573062X.2015.1076857.

Dirksen, J., Clemens, F. H. L. R., Korving, H., Cherqui, F., Le Gauffre, P., Ertl, T. … Snaterse, C. T. M. “The consistency of visual sewer inspection data.” Structure and Infrastructure Engineering 9 (2013): 214-228. doi: 10.1080/15732479.2010.541265.

Eiswirth, M., Heske, C., Hötzl, H., Schneider, T., Burn, L.S. “Pipe defect characterisation by multi-sensor systems.” Proceedings of 18th International Conference of No-Dig (October 2000).

Ashley RM, Fraser A, Burrows R, Blanksby J. “The management of sediment in combined sewers.” Urban Water 2 (December 2000): 263-275. doi:10.1016/S1462-0758(01)00010-3.

Arthur S, Ashley RM, Nalluri C. “Near bed solids transport in sewers.” Water Science and Technology 33 (1996): 69-76. doi:10.1016/0273-1223(96)00371-x.

Arthur S, Ashley RM. “The influence of near bed solids transport on first foul flush in combined sewers.” Water Science and Technology 37 (1998): 131-138. doi:10.1016/s0273-1223(97)00762-2.

Ebtehaj, I., Bonakdari, H., & Zaji, A. H. “A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes.” Alexandria Engineering Journal 57 (2018): 1783–1795. doi: 10.1016/j.aej.2017.05.021.

Seco I. “In-sewer organic sediment transport : study of the release of sediments during wet-weather from combined sewer systems in the Mediterranean region in Spain. PhD Thesis, Universitat Politècnica de Catalunya” (2014).

Central Statistical Organization- Iraq “(Iraqi Environmental statistics) Water, Wastewater, Municipal Services) For the Year 2015.” (2016).

Baghdad Sewage Authority “Baghdad Sewage Projects Details” (2017).

Konishi, Sadanori. “Introduction to Multivariate Analysis: Linear and Nonlinear Modeling, Second Edition” (June 6, 2014). doi: 10.1201/b17077.

Grove, Dan, Y. Sakamoto, M. Ishiguro, and G. Kitagawa. “Akaike information criterion statistics.” The Statistician 37 (1988): 477. doi:10.2307/2348776.

Schwarz, Gideon. “Estimating the Dimension of a Model.” The Annals of Statistics 6 (March 1978): 461-464. doi:10.1214/aos/1176344136.

Snee, Ronald D. “Validation of Regression Models: Methods and Examples.” Technometrics 19 (November 1977): 415-428. doi:10.2307/1267881.

Intrator, Orna, and Nathan Intrator. “Interpreting neural-network results: a simulation study.” Computational Statistics & Data Analysis 37 (September 2001): 373-393. doi:10.1016/S0167-9473(01)00016-0.

Braspenning, P. J., F. Thuijsman, and A. J. M. M. Weijters, eds. “Artificial Neural Networks.” Lecture Notes in Computer Science (1995). doi:10.1007/bfb0027019.

Zaccone,Giancarlo. “Getting Started with TensorFlow” (July 2016). ISBN 978-1-78646-857-4 , url:

IBM corporation “IBM SPSS Neural Networks 21” (2013).

Hornik, Kurt, Maxwell Stinchcombe, and Halbert White. “Multilayer Feedforward Networks Are Universal Approximators.” Neural networks 2 (January 1989): 359–366. doi:10.1016/0893-6080(89)90020-8

Garson, G. David. “Interpreting Neural-Network Connection Weights.” AI expert 6 (1991): 46–51. doi:10.1207/s15327752jpa8502.

Full Text: PDF

DOI: 10.28991/cej-2019-03091227


  • There are currently no refbacks.

Copyright (c) 2019 RAMI RAAD AHMED ALANI, Basim Hussein Khudair Al-Obaidi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.