An Artificial Neural Network Model for Water Quality and Water Consumption Prediction
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/14/21/3359 |
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| author | Furqan Rustam Abid Ishaq Sayyida Tabinda Kokab Isabel de la Torre Diez Juan Luis Vidal Mazón Carmen Lili Rodríguez Imran Ashraf |
| author_facet | Furqan Rustam Abid Ishaq Sayyida Tabinda Kokab Isabel de la Torre Diez Juan Luis Vidal Mazón Carmen Lili Rodríguez Imran Ashraf |
| author_sort | Furqan Rustam |
| collection | DOAJ |
| description | 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches. |
| first_indexed | 2024-03-09T18:34:46Z |
| format | Article |
| id | doaj.art-e666147e4dfd467d9d5e2e26939189bf |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-09T18:34:46Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-e666147e4dfd467d9d5e2e26939189bf2023-11-24T07:18:15ZengMDPI AGWater2073-44412022-10-011421335910.3390/w14213359An Artificial Neural Network Model for Water Quality and Water Consumption PredictionFurqan Rustam0Abid Ishaq1Sayyida Tabinda Kokab2Isabel de la Torre Diez3Juan Luis Vidal Mazón4Carmen Lili Rodríguez5Imran Ashraf6School of Computer Science, University College Dublin, D04 V1W8 Dublin, IrelandDepartment of Computer Science & Information Technology, The Islamia University of Bahwalpur, Bahwalpur 63100, PakistanDepartment of Computer Science, COMSATS University Islamabad, Islamabad 44000, PakistanDepartment of Signal Theory and Communications and Telematic Engineering, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, SpainHigher Polytechnic School, Universidad Europea del Atlántico, Parque Científico y Tecnológico de Cantabria, Isabel Torres 21, 39011 Santander, SpainHigher Polytechnic School, Universidad Europea del Atlántico, Parque Científico y Tecnológico de Cantabria, Isabel Torres 21, 39011 Santander, SpainDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, KoreaWith 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score is obtained for water consumption prediction which is superior to existing state-of-the-art approaches.https://www.mdpi.com/2073-4441/14/21/3359water quality predictionwater consumption predictionartificial neural networkclassification |
| spellingShingle | Furqan Rustam Abid Ishaq Sayyida Tabinda Kokab Isabel de la Torre Diez Juan Luis Vidal Mazón Carmen Lili Rodríguez Imran Ashraf An Artificial Neural Network Model for Water Quality and Water Consumption Prediction Water water quality prediction water consumption prediction artificial neural network classification |
| title | An Artificial Neural Network Model for Water Quality and Water Consumption Prediction |
| title_full | An Artificial Neural Network Model for Water Quality and Water Consumption Prediction |
| title_fullStr | An Artificial Neural Network Model for Water Quality and Water Consumption Prediction |
| title_full_unstemmed | An Artificial Neural Network Model for Water Quality and Water Consumption Prediction |
| title_short | An Artificial Neural Network Model for Water Quality and Water Consumption Prediction |
| title_sort | artificial neural network model for water quality and water consumption prediction |
| topic | water quality prediction water consumption prediction artificial neural network classification |
| url | https://www.mdpi.com/2073-4441/14/21/3359 |
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