dc.contributor.author | Rustam, Furqan | |
dc.contributor.author | Ishaq, Abid | |
dc.contributor.author | Kokab, Sayyida Tabinda | |
dc.contributor.author | Torre Díez, Isabel de la | |
dc.contributor.author | Vidal Mazón, Juan Luis | |
dc.contributor.author | Rodríguez, Carmen Lili | |
dc.contributor.author | Ashraf, Imran | |
dc.date.accessioned | 2023-09-19T10:55:05Z | |
dc.date.available | 2023-09-19T10:55:05Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Water, 2022, Vol. 14, Nº. 21, 3359 | es |
dc.identifier.issn | 2073-4441 | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/61641 | |
dc.description | Producción Científica | es |
dc.description.abstract | With rapid urbanization, high rates of industrialization, and inappropriate waste disposal, water quality has been substantially degraded during the past decade. So, water quality prediction, an essential element for a healthy society, has become a task of great significance to protecting the water environment. Existing approaches focus predominantly on either water quality or water consumption prediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increase computational complexity. This study proposes a simple architecture of neural networks which is more efficient and accurate and can work for predicting both water quality and water consumption. An artificial neural network (ANN) consisting of one hidden layer and a couple of dropout and activation layers is utilized in this regard. The approach is tested using two datasets for predicting water quality and water consumption. Results show a 0.96 accuracy for water quality prediction which is better than existing studies. A 0.99 R2 score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Water quality | es |
dc.subject | Water quality monitoring | es |
dc.subject | Agua - Calidad - Control | es |
dc.subject | Water consumption | es |
dc.subject | Agua - Consumo | es |
dc.subject | Water-supply | es |
dc.subject | Agua - Abastecimiento | es |
dc.subject | Neural networks (Computer science) | es |
dc.subject | Redes neuronales (Informática) | es |
dc.subject | Classification | es |
dc.subject | Artificial intelligence | |
dc.title | An artificial neural network model for water quality and water consumption prediction | es |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.holder | © 2022 The Authors | es |
dc.identifier.doi | 10.3390/w14213359 | es |
dc.relation.publisherversion | https://www.mdpi.com/2073-4441/14/21/3359 | es |
dc.identifier.publicationfirstpage | 3359 | es |
dc.identifier.publicationissue | 21 | es |
dc.identifier.publicationtitle | Water | es |
dc.identifier.publicationvolume | 14 | es |
dc.peerreviewed | SI | es |
dc.identifier.essn | 2073-4441 | es |
dc.rights | Atribución 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es |
dc.subject.unesco | 1203.04 Inteligencia Artificial | es |
dc.subject.unesco | 2508.11 Calidad de las Aguas | es |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional