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

Abstract

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.

A review of pollution-based real-time modelling and control for sewage systems

“…In this section, data-driven models utilised in combination with RTC systems are presented. One notable work is the publication of [1] , which studies different deep learning algorithms to predict several water quality parameters. The paper proposed several models used to predict sedimentation and compare the results using five deep learning algorithms: Multi Linear Regression (MLR), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-term Memory (LSTM) and Recurrent Gate Unit (GRU).…”

Numerical Study on RC Multilayer Perforation with Application to GA-BP Neural Network Investigation

“…For a general BP neural network, the initial weights and thresholds are randomly selected in the interval (-1, 1). This paper uses genetic algorithm to optimize the BP neural network [18,21]. The initial population is defined as 50, the number of evolutions is 100, the crossover probability is 0.8, and the mutation probability is 0.09.The optimal individual obtained is decoded as the initial weight and threshold of the BP neural network.…”

Experimental Modeling and Evaluation Sediment Scouring in Riverbeds around Downstream in Flip Buckets

“…Then, as also illustrated in Figures 9 and 10, the calculated through this list was compared to the empirical ds. There is a linear relationship between the estimated depth obtained from experimental formulas and the scour depth collected in the laboratory, as shown in Equation (11).…”

“…[4,5]. So far several studies have been carried out to address scouring downstream of flip bucket jets by Bormann and Julien [6]; Jafari and Khiavi [7], Mason and Arumugan [8], Amanian and Urroz [9], Stein et al [10], Afify and Urroz [11] and Cordier et al [12]. Furthermore, scholars such as Hoffmans and Verheij [13], Khalifehei et al [14], Ghodsian et al [15], Juon and Hager [16], Pagliara et al [17], Yamini [18], and Movahedi et al [19] used experimental data to provide empirical predictive relationships for the maximum depth of scour caused by the jet.…”