Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks
Variability in water use and user characteristics influences the operational management of water distribution systems (WDS). Types of water use and external factors including socioeconomic characteristics and weather variables can affect the normal operation of WDS. Accurate demand prediction is cru...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/155 |
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| author | Jorge E. Pesantez Morgan DiCarlo Fayzul Pasha Emily Z. Berglund |
| author_facet | Jorge E. Pesantez Morgan DiCarlo Fayzul Pasha Emily Z. Berglund |
| author_sort | Jorge E. Pesantez |
| collection | DOAJ |
| description | Variability in water use and user characteristics influences the operational management of water distribution systems (WDS). Types of water use and external factors including socioeconomic characteristics and weather variables can affect the normal operation of WDS. Accurate demand prediction is crucial, yet existing methods lack industry-wide comparability. This study applies a supervised learning model, IONET, that utilizes feedforward neural networks for short-term demand forecasting. IONET incorporates lagged demand, seasonal predictors, and weather variables. Tested on Italian DMA data, it swiftly produces accurate forecasts across various horizons. Feature importance analysis underscores the significance of seasonal variables and lagged demand. The IONET model offers prompt training and valuable insights for optimizing WDS management, facilitating the digital transformation of water infrastructure. |
| format | Article |
| id | doaj-art-a88ec937cc6c439e85c2aa89a8a95e1a |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-a88ec937cc6c439e85c2aa89a8a95e1a2025-08-20T02:42:45ZengMDPI AGEngineering Proceedings2673-45912024-09-0169115510.3390/engproc2024069155Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban NetworksJorge E. Pesantez0Morgan DiCarlo1Fayzul Pasha2Emily Z. Berglund3Department of Civil and Geomatics Engineering, California State University Fresno, Fresno, CA 93740, USAAmerican Association for the Advancement of Science Fellowship Programs, Washington, DC 20005, USADepartment of Civil and Geomatics Engineering, California State University Fresno, Fresno, CA 93740, USADepartment of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27606, USAVariability in water use and user characteristics influences the operational management of water distribution systems (WDS). Types of water use and external factors including socioeconomic characteristics and weather variables can affect the normal operation of WDS. Accurate demand prediction is crucial, yet existing methods lack industry-wide comparability. This study applies a supervised learning model, IONET, that utilizes feedforward neural networks for short-term demand forecasting. IONET incorporates lagged demand, seasonal predictors, and weather variables. Tested on Italian DMA data, it swiftly produces accurate forecasts across various horizons. Feature importance analysis underscores the significance of seasonal variables and lagged demand. The IONET model offers prompt training and valuable insights for optimizing WDS management, facilitating the digital transformation of water infrastructure.https://www.mdpi.com/2673-4591/69/1/155water demand managementartificial neural networksdemand forecastingDistrict Metering Areas |
| spellingShingle | Jorge E. Pesantez Morgan DiCarlo Fayzul Pasha Emily Z. Berglund Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks Engineering Proceedings water demand management artificial neural networks demand forecasting District Metering Areas |
| title | Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks |
| title_full | Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks |
| title_fullStr | Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks |
| title_full_unstemmed | Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks |
| title_short | Predictive Model for Short-Term Water Demand Forecasting and Feature Analysis in Urban Networks |
| title_sort | predictive model for short term water demand forecasting and feature analysis in urban networks |
| topic | water demand management artificial neural networks demand forecasting District Metering Areas |
| url | https://www.mdpi.com/2673-4591/69/1/155 |
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