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Detection of Water Safety Conditions in Distribution Systems Based on Artificial Neural Network and Support Vector Machine

Mohammed, Hadi; Hameed, Ibrahim A.; Seidu, Razak
Peer reviewed, Journal article
Published version
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Mohammed (Locked)
URI
https://hdl.handle.net/11250/2660000
Date
2019
Metadata
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  • Institutt for havromsoperasjoner og byggteknikk [432]
  • Institutt for IKT og realfag [243]
  • Publikasjoner fra CRIStin - NTNU [19849]
Original version
Advances in Intelligent Systems and Computing. 2019, 845 567-576.   10.1007/978-3-319-99010-1_52
Abstract
This study presents the development of artificial neural network (ANN) and support vector machine (SVM) classification models for predicting the safety conditions of water in distribution pipes. The study was based on 504 monthly records of water quality parameters; pH, turbidity, color and bacteria counts taken from nine different locations across the water distribution network in the city of Ålesund, Norway. The models predicted the safety conditions of the water samples in the pipes with 98% accuracy and 94% respectively during testing. The high accuracy achieved in the model results indicate that contamination events in distribution systems that result in unsafe values of the water quality parameters can be detected using these classification models. This can provide water utility managers with real time information about the safety conditions of treated water at different locations of distribution pipes before water reaches consumers.
Publisher
Springer
Journal
Advances in Intelligent Systems and Computing

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