RT info:eu-repo/semantics/article
T1 An artificial neural network model for water quality and water consumption prediction
A1 Rustam, Furqan
A1 Ishaq, Abid
A1 Kokab, Sayyida Tabinda
A1 Torre Díez, Isabel de la
A1 Vidal Mazón, Juan Luis
A1 Rodríguez, Carmen Lili
A1 Ashraf, Imran
K1 Water quality
K1 Water quality monitoring
K1 Agua - Calidad - Control
K1 Water consumption
K1 Agua - Consumo
K1 Water-supply
K1 Agua - Abastecimiento
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Classification
K1 Artificial intelligence
K1 1203.04 Inteligencia Artificial
K1 2508.11 Calidad de las Aguas
AB 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 consumptionprediction, utilizing complex algorithms that reduce the accuracy of imbalanced datasets and increasecomputational 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.
PB MDPI
SN 2073-4441
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61641
UL https://uvadoc.uva.es/handle/10324/61641
LA eng
NO Water, 2022, Vol. 14, Nº. 21, 3359
NO Producción Científica
DS UVaDOC
RD 09-may-2024