@inproceedings{e8dcdf63d878432da42d68a534e30d46,
title = "Real-Time Multi-Class Classification of Water Quality Using MLP and Ensemble Learning",
abstract = "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.",
keywords = "Classification, Ensemble learning, MLP, Water Quality",
author = "Shahra, {Essa Q.} and Shadi Basurra and Wenyan Wu",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 8th International Congress on Information and Communication Technology, ICICT 2023 ; Conference date: 20-02-2023 Through 23-02-2023",
year = "2023",
month = oct,
day = "26",
doi = "10.1007/978-981-99-3043-2_37",
language = "English",
isbn = "9789819930425",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "481--491",
editor = "Xin-She Yang and Sherratt, {R. Simon} and Nilanjan Dey and Amit Joshi",
booktitle = "Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023",
}