Deep Learning for Water Quality Classification in Water Distribution Networks

Shahra, Essa and Wu, Wenyan and Basurra, Shadi and Rizuo, Stamatia (2021) Deep Learning for Water Quality Classification in Water Distribution Networks. In: EANN 22nd International Conference on Engineering Applications of Neural Networks.

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Maintaining high water quality is the main goal for water management planning and iterative evaluation of operating policies. For effective water monitoring, it is crucial to test a vast number of drinking water samples that is time-consuming and labor-intensive. The primary objective of this study is to determine, with high accuracy, the quality of drinking water samples by machine learning classification models while keeping computation time low. This paper aims to investigate and evaluate the performance of two supervised classification algorithms, including artificial neural network (ANN) and support vector machine (SVM) for multiclass water classification. The evaluation uses the confusion matrix that includes all metrics ratios, such as true positive, true negative, false positive, and false negative. Moreover, the overall accuracy and f1-score of the models are evaluated. The results demonstrate that ANN outperformed the SVM with an overall accuracy of 94% in comparison to SVM, which shows an overall accuracy of 89%.

Item Type: Conference or Workshop Item (Paper)
Identification Number:
Date: 22 June 2021
Uncontrolled Keywords: water distribution system Water quality Classification SVM ANN
Subjects: G500 Information Systems
H100 General Engineering
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Engineering and the Built Environment
Depositing User: Wenyan Wu
Date Deposited: 16 Jul 2021 08:37
Last Modified: 16 Jul 2021 08:37

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