Artificial Neural Networks

Braspenning, Peter J.; Thuijsman, Frank; Weijters, A. J. M. M.

doi:10.1007/bfb0027019

Cited by 100 publications

(2 citation statements)

References 0 publications

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“…There are several advantages of using a ANN-MLP, but, specifically for a sediment accumulation model, the ability to adapt to complex relationships between dependant and independent variables and to changes that occur in the process is very unique [11] . Besides that, there are many open-source software packages available for implementing ANNś, such as scikit-learn, a simple library of machine learning methods using python [59] and OpenNN, a neural network library for C++ [50] .…”

Section: Modelling Of Sewage Systems For Controlmentioning

confidence: 99%

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

da Silva Gesser,

Voos,

Cornelissen

et al. 2024

Heliyon

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Section: Modelling Of Sewage Systems For Controlmentioning

confidence: 99%

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

da Silva Gesser,

Voos,

Cornelissen

et al. 2024

Heliyon

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“…ANN is a very flexible and powerful tool, usually used when conventional statistical and mathematical methods fail due to the unfulfilled boundary conditions. ANNs are not theoretically supported, nevertheless, still showing practical significance [22]. ANN comprises a number of layers each layer consists of nodes arranged in one level, each node (neurons) has a simple task defined by an activation function.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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

Al-Ani

Al-Obaidi

2019

Civ Eng J

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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.

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