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Extreme learning machines for detecting the water quality for human consumption
| dc.contributor.author | Barría Valdebenito, Pedro | |
| dc.contributor.author | Zabala-Blanco, David | |
| dc.contributor.author | Ahumada-García, Roberto | |
| dc.contributor.author | Soto, Ismael | |
| dc.contributor.author | Dehghan Firoozabadi, Ali | |
| dc.contributor.author | Flores-Calero, Marco | |
| dc.date.accessioned | 2023-10-25T13:11:49Z | |
| dc.date.available | 2023-10-25T13:11:49Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://repositorio.ucm.cl/handle/ucm/5041 | |
| dc.description.abstract | Determining the potability of water for consumption is crucial for human health. To assess the water quality, levels of minerals and ions are measured, such as pH, hardness, sodium, chloramines, sulfate, conductivity, organic carbon, tri-halomethanes, and turbidity. To achieve this more efficiently and accurately, techniques of Machine Learning (ML) and Deep Learning (DL) have been applied, with deep neural networks being one of the most popular methods. In this study, Extreme Learning Machines (ELM) are evaluated for the first time, including the standard ELM, the Regularized ELM, the weighted ELMs in configurations 1 and 2, and the multi-layer ELM. Accuracy and the G-mean were used to extensively compare the results and it was found that the weighted ELM 1 is the most recommended algorithm for the binary classification of the potability of water for human consumption, with an accuracy of 75.8% and a G-mean of 80.6%. The feasibility of using ELMs to determine the potability of water is thus demonstrated, as they offer acceptable performance and low computational cost for training. | es_CL |
| dc.language.iso | en | es_CL |
| dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Chile | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/cl/ | * |
| dc.source | IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI), 2023, 1-6 | es_CL |
| dc.subject | Extreme learning machines | es_CL |
| dc.subject | Deep learning | es_CL |
| dc.subject | Training | es_CL |
| dc.subject | Standards | es_CL |
| dc.subject | Sodium | es_CL |
| dc.subject | Python | es_CL |
| dc.subject | Predictive models | es_CL |
| dc.title | Extreme learning machines for detecting the water quality for human consumption | es_CL |
| dc.type | Article | es_CL |
| dc.ucm.indexacion | Scopus | es_CL |
| dc.ucm.uri | ieeexplore.ieee.org/document/10225820 | es_CL |
| dc.ucm.doi | doi.org/10.1109/ColCACI59285.2023.10225820 | es_CL |
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