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 11-oct-2024