Real-Time Multi-Class Classification of Water Quality Using MLP and Ensemble Learning

Shahra, Essa and Basurra, Shadi and Wu, Wenyan (2023) Real-Time Multi-Class Classification of Water Quality Using MLP and Ensemble Learning. In: 8th International Congress on Information and Communication Technology, 20th - 23rd February 2023, London, UK.

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The major goal of water management planning and the iterative evaluation of operational policies and procedures is to ensure that good water quality is always maintained. Effective water monitoring requires examining many water samples, which is a time-consuming and labour-intensive process that takes a lot of effort. This paper aims to evaluate the quality of drinking water samples with high accuracy by using multi-class classification models: multilayer perceptron (MLP) and ensemble learning. Real datasets with different sizes that include the essential water quality parameters have been used to train and test the developed models. The results showed the effectiveness of the developed models in detecting water contamination with high accuracy in both datasets used. The results demonstrate that bagging Ensemble learning outperforms the multilayer perceptron with an overall accuracy of 94% for station-A and 92% for station-B compared to MLP, which shows an overall accuracy of 89% for station-A and 87% for station-B.

Item Type: Conference or Workshop Item (Paper)
13 December 2022Accepted
23 October 2023Published Online
Uncontrolled Keywords: Classification, Ensemble learning, MLP, Water Quality
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Essa Shahra
Date Deposited: 11 Apr 2023 14:45
Last Modified: 09 Aug 2023 15:34

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