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Title: Optimizing Water Treatment Systems Using Artificial Intelligence Based Tools
Authors: Pinto, Ana Mafalda
Fernandes, Ana
Vicente, Henrique
Neves, José
Editors: Brebbia, C.A.
Popov, V.
Keywords: Knowledge Discovery from Databases
Data Mining
Decision Trees
Water Quality
Issue Date: 2009
Publisher: WIT Press
Citation: Pinto, A., Fernandes, A.V., Vicente, H. & Neves, J., Optimizing Water Treatment Systems Using Artificial Intelligence Based Tools. In C. A. Brebbia & V. Popov Eds., Water Resourse Management V, WIT Transactions on Ecology and the Environment, Vol. 125, pp. 185–194, WIT Press, Southampton, United Kingdom, 2009.
Abstract: Predictive modelling is a process used in predictive analytics to create a statistical model of future behaviour. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. On the other hand, Artificial Intelligence (AI) concerns itself with intelligent behaviour, i.e. the things that make us seem intelligent. Following this process of thinking, in this work the main goal is the assessment of the impact of using AI based tools for the development of intelligent predictive models, in particular those that may be used to establish the conditions in which the levels of manganese and turbidity in water supply are high. Indeed, one of the main problems that the water treatment plant at Monte Novo (in Évora, Portugal) uncovers is the appearance of high levels of manganese and turbidity in treated water, which sometimes exceed the parametric values established in Portuguese Law, respectively 50 μg dm-3 and 4 NTU. In this study we tried to find answers to the above problem by building predictive models. The models we developed shall enable the prediction of manganese and turbidity levels in treated water, in order to ensure that the water supply does not affect public health in a negative way and obeys the current legislation. The software used in this study was the Clementine 11.1. The C5.0 Algorithm was also used as a means of introducing Decision Trees and the KMeans Algorithm was used to construct clustering models. The data in the database was collected from 2005 to 2006 and includes reservoir water quality data, treated water data and volumes of water stored in the reservoir.
ISBN: 978-1-84564-199-3
ISSN: 1743-3541
Type: bookPart
Appears in Collections:QUI - Publicações - Capítulos de Livros
CQE - Publicações - Capítulos de Livros

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