Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/37422

TitleWastewater treatment plant performance prediction with support vector machines
Author(s)Ribeiro, Daniel
Sanfins, António
Belo, O.
KeywordsData Mining
Regression Techniques
Wastewater Treatment Plants
Support Vector Machines
Biochemical Oxygen Demand
Total Suspended Solids Analysis
Issue date16-Jul-2013
JournalLecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract(s)Wastewater treatment plants are essential infrastructures to maintain the environmental balance of the regions where they were installed. The dynamic and complex wastewater treatment procedure must be handled efficiently to ensure good quality effluents. This paper presents a research and development work implemented to predict the performance of a wastewater treatment plant located in the northern Portugal, serving a population of about 45,000 inhabitants. The data we used were recorded based on the daily averaged values of the measured parameters during the period of one year. The predictive models were developed supported by two implementations of Support Vector Machines methods for regression, due to the presence of two lines of treatment in the selected case of study, using two of the most relevant output parameters of a wastewater treatment plant: the biochemical oxygen demand and the total suspended solids. We describe here the wastewater treatment plant we studied as well the data sets used in the mining processes, analyzing and comparing the regression models for both predictive parameters that were selected.
TypeConference paper
URIhttp://hdl.handle.net/1822/37422
ISBN9783642397356
DOI10.1007/978-3-642-39736-3_8
ISSN0302-9743
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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