eprintid: 5425 rev_number: 12 eprint_status: archive userid: 2 dir: disk0/00/00/54/25 datestamp: 2023-01-13 23:30:07 lastmod: 2023-07-11 23:31:10 status_changed: 2023-01-13 23:30:07 type: article metadata_visibility: show creators_name: Rustam, Furqan creators_name: Ishaq, Abid creators_name: Kokab, Sayyida Tabinda creators_name: de la Torre Diez, Isabel creators_name: Vidal Mazón, Juan Luis creators_name: Rodríguez Velasco, Carmen Lilí creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: juanluis.vidal@uneatlantico.es creators_id: carmen.rodriguez@uneatlantico.es creators_id: title: An Artificial Neural Network Model for Water Quality and Water Consumption Prediction ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: water quality prediction; water consumption prediction; artificial neural network; classification 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. date: 2022-10 publication: Water volume: 14 number: 21 pagerange: 3359 id_number: doi:10.3390/w14213359 refereed: TRUE issn: 2073-4441 official_url: http://doi.org/10.3390/w14213359 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés 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. metadata Rustam, Furqan; Ishaq, Abid; Kokab, Sayyida Tabinda; de la Torre Diez, Isabel; Vidal Mazón, Juan Luis; Rodríguez Velasco, Carmen Lilí y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, juanluis.vidal@uneatlantico.es, carmen.rodriguez@uneatlantico.es, SIN ESPECIFICAR (2022) An Artificial Neural Network Model for Water Quality and Water Consumption Prediction. Water, 14 (21). p. 3359. ISSN 2073-4441 document_url: http://repositorio.uneatlantico.es/id/eprint/5425/1/water-14-03359-v2.pdf