ANFIS-based approach for predicting sediment transport in clean sewer

Abstract

The necessity of sewers to carry sediment has been recognized for many years. Typically, old sewage systems were designated based on self-cleansing concept where there is no deposition in sewer. These codes were applicable to non-cohesive sediments (typically storm sewers). This study presents adaptive neuro-fuzzy inference system (ANFIS), which is a combination of neural network and fuzzy logic, as an alternative approach to predict the functional relationships of sediment transport in sewer pipe systems. The proposed relationship can be applied to different boundaries with partially full flow. The present ANFIS approach gives satisfactory results (r2 = 0.98 and RMSE = 0.002431) compared to the existing predictor.

Gene expression programming to predict the discharge coefficient in rectangular side weirs

“…Soft computing, a powerful tool in modeling and solving complex nonlinear problems in different fields such as hydraulic engineering [26,27], river engineering [28], and sediment transport [29,30], has come to the attention of a number of researchers in recent years. By using neural networks and neuro-fuzzy technique, Kisi [31] estimated daily suspended sediment load.…”

Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers

“…According to , the above equation is the best for sediment transport at limit of deposition. Azamathulla et al (2012) amended the Ab. Ghani A (1993) equation as follows:…”

“…The sediment transport equations are presented in two forms of dimensional analysis and semiexperimental analysis. Different semi-experimental equations are proposed using the solution of forces affecting the particle at equilibrium and equations obtained from the dimensional analysis of the proposed dimensionless groups of different studies (Nalluri 1985;Mayerle et al 1991;Azamathulla et al 2012). Existing transport equations which were all obtained from regression do not show the same results under different conditions.…”

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

“…Several studies in this field (Ab Ghani 1993;Vongvisessomjai et al 2010;Azamathulla et al 2012;Ebtehaj and Bonakdari 2013) presented the influential parameters on sediment transport in non-deposition state as follows:…”