Evaluation of Sediment Transport in Sewer using Artificial Neural Network

Abstract Sedimentation in sewers occurs regularly according to the alternating natural flow. The long term deposit of material in the sewerage systems increases the risk of changes in the sediments and their consolidation and cementation. In particular under low flow conditions, permanent settlement similar to that on the sewer bed alters the nature of velocity and distribution of the boundary shear stress. Consequently, it affects the capacity of sediment transport and the hydraulic resistance of the sewer. The article reviews the application of Artificial Neural Network (ANN) in predicting the sediment transport using the concept of self-cleansing of sewer systems. In comparison with existing methods, the ANN showed acceptable results.

[1]  Gokmen Tayfur,et al.  Artificial neural networks for estimating daily total suspended sediment in natural streams , 2006 .

[2]  P. P. Mujumdar,et al.  A fuzzy dynamic wave routing model , 2008 .

[3]  Richard May,et al.  Self-Cleansing Sewer Design Based on Sediment Transport Principles , 2003 .

[4]  Zekai Şen,et al.  A comparative fuzzy logic approach to runoff coefficient and runoff estimation , 2006 .

[5]  Sharad K. Jain,et al.  Development of Integrated Sediment Rating Curves Using ANNs , 2001 .

[6]  Aminuddin Ab. Ghani.  Sediment transport in sewers , 1993 .

[7]  Amir Jalalkamali,et al.  Application of Hybrid Neural Modeling and Radial Basis Function Neural Network to Estimate Leakage Rate in Water Distribution Network , 2011 .

[8]  Hadi Memarian,et al.  Comparison between Multi-Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed , 2012 .

[9]  Aminuddin Ab. Ghani,et al.  Sediment transport over deposited beds in sewers , 1994 .

[10]  Richard May,et al.  Development of design methodology for self-cleansing sewers , 1996 .

[11]  C Nalluri,et al.  SEDIMENT TRANSPORT IN SMOOTH FIXED BED CHANNELS , 1975 .

[12]  P. Novak,et al.  Sediment transport in rigid bed conveyances , 1991 .

[13]  O. Ks.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation , 2004 .

[14]  Hossein Bonakdari,et al.  Closed-Form Solution for Flow Field in Curved Channels in Comparison with Experimental and Numerical Analyses and Artificial Neural Network , 2012 .

[15]  P. M. Brown,et al.  Self-cleansing conditions for sewers carrying sediment , 1989 .

[16]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[17]  Mohammad Muzzammil,et al.  Application of Neural Networks To Scour Depth Prediction at The Bridge Abutments , 2008 .

[18]  Markus Disse,et al.  Fuzzy rule-based models for infiltration , 1993 .

[19]  Narendra Singh Raghuwanshi,et al.  Runoff and Sediment Yield Modeling using Artificial Neural Networks: Upper Siwane River, India , 2006 .

[20]  O. Kisi.  River flow forecasting and estimation using different artificial neural network techniques , 2008 .

[21]  C. Chang,et al.  Parameter Sensitivity Analysis of Artificial Neural Network for Predicting Water Turbidity , 2012 .

[22]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[23]  Davar Khalili,et al.  Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions , 2010 .

[24]  Özgür Kişi,et al.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt , 2004 .

[25]  Mukand S. Babel,et al.  Non-deposition design criteria for sewers with part-full flow , 2010 .

[26]  David Butler,et al.  Designing Sewers to Control Sediment Problems , 2001 .

[27]  Chuntian Cheng,et al.  Using support vector machines for long-term discharge prediction , 2006 .

[28]  Mohammad Taghi Aalami,et al.  Evaluation of total load sediment transport formulas using ANN , 2009 .

[29]  Thomas Jackson,et al.  Neural Computing - An Introduction , 1990 .

[30]  Hikmet Kerem Ciğizoğlu,et al.  Suspended Sediment Estimation and Forecasting using Artificial Neural Networks , 2002 .

[31]  H. Md. Azamathulla,et al.  ANFIS-based approach for predicting sediment transport in clean sewer , 2012, Appl. Soft Comput..

[32]  Hikmet Kerem Ciğizoğlu,et al.  Suspended Sediment Estimation for Rivers using Artificial Neural Networks and Sediment Rating Curves , 2002 .

[33]  Mohammad Ranjbar,et al.  Comparison of Artificial Neural Networks ANN and Statistica in Daily Flow Forecasting , 2012 .

[34]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[35]  Hossein Bonakdari,et al.  Numerical Analysis and Prediction of the Velocity Field in Curved Open Channel Using Artificial Neural Network and Genetic Algorithm , 2011 .

[36]  Chuntian Cheng,et al.  A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series , 2009 .

[37]  Fred Joseph Gruenberger,et al.  Computing: An Introduction , 1969 .