Predicting performance of vegetative filter strips using artificial neural networks

dc.contributor.advisorNegi, S.C.
dc.contributor.authorAndriyas, Sanyogita
dc.date.accessioned2020-08-24T15:44:08Z
dc.date.available2020-08-24T15:44:08Z
dc.date.copyright2003
dc.degree.departmentSchool of Engineeringen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.description.abstractThe vegetative filter strips (VFS) are a best management practice. For quantifying the movement and amount of sediments and nutrients, the performance of VFS has to be modelled. Here artificial neural networks (ANN'S) were investigated, to model VFS and then compared to GRAPH. Data available from the literature and recent experiments were used. Artificial runoff was created. Flow samples were analysed for concentrations for total suspended solids, total phosphorus and soluble phosphorus, and particle size distribution. Available input-output data sets were used to train and test a multi-layered perceptron using back propagation (BP) algorithm and a radial basis function neural network using fuzzy c-means clustering algorithm. Sensitivity tests were done for finding optimum architectures of neural networks. The statistical analysis and comparisons between predicted and observed values for the three models showed that a BP network with 15 hidden units can model the performance of VFS efficiently, including the trapping of soluble P. They could predict the outputs, even without the particle size distribution. ANN'S have to be trained before being used to predict the outputs. GRAPH is mobile and could be successfully used for verification, since it takes into account the physical processes going on.en_US
dc.identifier.urihttps://hdl.handle.net/10214/19762
dc.language.isoen
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectNeural networksen_US
dc.subjectArtificialen_US
dc.subjectVegetative filter stripsen_US
dc.subjectPerformanceen_US
dc.subjectSensitivityen_US
dc.titlePredicting performance of vegetative filter strips using artificial neural networksen_US
dc.typeThesisen_US

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