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Título
An artificial neural network model for water quality and water consumption prediction
Autor
Año del Documento
2022
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Water, 2022, Vol. 14, Nº. 21, 3359
Resumo
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.
Materias (normalizadas)
Water quality
Water quality monitoring
Agua - Calidad - Control
Water consumption
Agua - Consumo
Water-supply
Agua - Abastecimiento
Neural networks (Computer science)
Redes neuronales (Informática)
Classification
Artificial intelligence
Materias Unesco
1203.04 Inteligencia Artificial
2508.11 Calidad de las Aguas
ISSN
2073-4441
Revisión por pares
SI
Version del Editor
Propietario de los Derechos
© 2022 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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