Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt
Background: This research paper studied the parameters affecting the non-revenue water in Egypt. The neural model was developed to forecast the NRW ratio before and after network rehabilitation using main parameters and statistical data for various measured district metered areas (DMAs) in Egypt. Th...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447921004512 |
| _version_ | 1828218654841372672 |
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| author | Mona Rafat Elkharbotly Mohamed Seddik Abdelkawi Khalifa |
| author_facet | Mona Rafat Elkharbotly Mohamed Seddik Abdelkawi Khalifa |
| author_sort | Mona Rafat Elkharbotly |
| collection | DOAJ |
| description | Background: This research paper studied the parameters affecting the non-revenue water in Egypt. The neural model was developed to forecast the NRW ratio before and after network rehabilitation using main parameters and statistical data for various measured district metered areas (DMAs) in Egypt. The non-revenue water (NRW) ratio is a parameter of significant concern for the water distribution system's performance evaluation. Therefore, NRW ratio parameter evaluation requires analysis of variables that influence the NRW ratio. Aim/objective: This study aims to offer findings that will lay the basis for sustainable water resources management in Egypt.Consequently, a comprehensive evaluation of water distribution systems and parameters affecting NRW was conducted. Methodology: The predictive models were developed using the historical data of the water company for various measured district metered areas (DMAs) in Egypt. The automated meta-models were developed at steady-state and over a prolonged period using a series of principles, rules, and constraints. It was based on multi hidden units of feed-forward neural networks trained by the back-propagation algorithm. Thus, the iterative methods of weight adjustment and activation function of Levenberg-Marquardt was used. Two scenarios were assumed, one that has complete data of the DMA investigated while the other assumed total lack of knowledge. Therefore, four models were built to forecast multiple parameters affecting the percentage of non-revenue water. However, three models were combined to reach the final NRW ratio with no historical data. An optimal number of neurons was determined to be 10. Each model result was tested against numeral statistical indicators showing a different level of accuracy. Results: The model results showed high accuracy in NRW final estimation. The performance indicators (i.e., RMSE, MAE, and correlation) also showed that the machine-learning algorithm better identifies complex relationships between different parameters. The models developed in this research can be applied to other DMAs in Egypt. Overall, these findings indicate that the machine-learning model may be adequate for water companies seeking immediate, cost-effective and long-term improvement of their water distribution systems. Practical implication: Results can provide the basis for decision-makers to reduce costs after applying the rehabilitation process upon any new DMA created. This study presents a modelling approach that provides the best prediction of non-revenue water. It allows the use of a model that enables the accurate projection of NRW ratio and approximating NRW in Egypt and elsewhere in similar countries around the world. Thus, it will help decision-makers enhance the overall NRW ratio strategically across Egypt and elsewhere worldwide. |
| first_indexed | 2024-04-12T16:06:08Z |
| format | Article |
| id | doaj.art-4cb97c9cbf754d149b616e351d5855ba |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-12T16:06:08Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-4cb97c9cbf754d149b616e351d5855ba2022-12-22T03:26:03ZengElsevierAin Shams Engineering Journal2090-44792022-09-01135101673Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in EgyptMona Rafat Elkharbotly0Mohamed Seddik1Abdelkawi Khalifa2Corresponding author.; Irrigation and Hydraulics Department, Faculty of Engineering, Ain Shams University, Cairo, EgyptIrrigation and Hydraulics Department, Faculty of Engineering, Ain Shams University, Cairo, EgyptIrrigation and Hydraulics Department, Faculty of Engineering, Ain Shams University, Cairo, EgyptBackground: This research paper studied the parameters affecting the non-revenue water in Egypt. The neural model was developed to forecast the NRW ratio before and after network rehabilitation using main parameters and statistical data for various measured district metered areas (DMAs) in Egypt. The non-revenue water (NRW) ratio is a parameter of significant concern for the water distribution system's performance evaluation. Therefore, NRW ratio parameter evaluation requires analysis of variables that influence the NRW ratio. Aim/objective: This study aims to offer findings that will lay the basis for sustainable water resources management in Egypt.Consequently, a comprehensive evaluation of water distribution systems and parameters affecting NRW was conducted. Methodology: The predictive models were developed using the historical data of the water company for various measured district metered areas (DMAs) in Egypt. The automated meta-models were developed at steady-state and over a prolonged period using a series of principles, rules, and constraints. It was based on multi hidden units of feed-forward neural networks trained by the back-propagation algorithm. Thus, the iterative methods of weight adjustment and activation function of Levenberg-Marquardt was used. Two scenarios were assumed, one that has complete data of the DMA investigated while the other assumed total lack of knowledge. Therefore, four models were built to forecast multiple parameters affecting the percentage of non-revenue water. However, three models were combined to reach the final NRW ratio with no historical data. An optimal number of neurons was determined to be 10. Each model result was tested against numeral statistical indicators showing a different level of accuracy. Results: The model results showed high accuracy in NRW final estimation. The performance indicators (i.e., RMSE, MAE, and correlation) also showed that the machine-learning algorithm better identifies complex relationships between different parameters. The models developed in this research can be applied to other DMAs in Egypt. Overall, these findings indicate that the machine-learning model may be adequate for water companies seeking immediate, cost-effective and long-term improvement of their water distribution systems. Practical implication: Results can provide the basis for decision-makers to reduce costs after applying the rehabilitation process upon any new DMA created. This study presents a modelling approach that provides the best prediction of non-revenue water. It allows the use of a model that enables the accurate projection of NRW ratio and approximating NRW in Egypt and elsewhere in similar countries around the world. Thus, it will help decision-makers enhance the overall NRW ratio strategically across Egypt and elsewhere worldwide.http://www.sciencedirect.com/science/article/pii/S2090447921004512Water distribution networksArtificial Neural NetworksNon-revenue waterMultiple regression AnalysisWater managementSustainability |
| spellingShingle | Mona Rafat Elkharbotly Mohamed Seddik Abdelkawi Khalifa Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt Ain Shams Engineering Journal Water distribution networks Artificial Neural Networks Non-revenue water Multiple regression Analysis Water management Sustainability |
| title | Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt |
| title_full | Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt |
| title_fullStr | Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt |
| title_full_unstemmed | Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt |
| title_short | Toward Sustainable Water: Prediction of non-revenue water via Artificial Neural Network and Multiple Linear Regression modelling approach in Egypt |
| title_sort | toward sustainable water prediction of non revenue water via artificial neural network and multiple linear regression modelling approach in egypt |
| topic | Water distribution networks Artificial Neural Networks Non-revenue water Multiple regression Analysis Water management Sustainability |
| url | http://www.sciencedirect.com/science/article/pii/S2090447921004512 |
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