An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas
This study introduces an innovative ensemble data-driven model designed for short-term water demand forecasting within urban areas. By synergistically combining three distinct machine learning approaches—NHiTS, XGBoost regression, and a multi-head 1D convolutional neural network—our model enhances f...
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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/2673-4591/69/1/69 |
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| author | Amin E. Bakhshipour Hossein Namdari Alireza Koochali Ulrich Dittmer Ali Haghighi |
| author_facet | Amin E. Bakhshipour Hossein Namdari Alireza Koochali Ulrich Dittmer Ali Haghighi |
| author_sort | Amin E. Bakhshipour |
| collection | DOAJ |
| description | This study introduces an innovative ensemble data-driven model designed for short-term water demand forecasting within urban areas. By synergistically combining three distinct machine learning approaches—NHiTS, XGBoost regression, and a multi-head 1D convolutional neural network—our model enhances forecasting accuracy and reliability. This integration not only leverages the unique strengths of each method but also compensates for their individual weaknesses, resulting in a robust solution for predicting urban water demand. Tested against the Battle of Water Demand Forecasting dataset (WDSA-CCWI-2024), our ensemble model demonstrates superior performance, offering a promising tool for efficient water resource management and decision making. |
| format | Article |
| id | doaj-art-a93a1e9411fc4bc6a56e613bfe848d43 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-a93a1e9411fc4bc6a56e613bfe848d432025-08-20T02:11:25ZengMDPI AGEngineering Proceedings2673-45912024-09-016916910.3390/engproc2024069069An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban AreasAmin E. Bakhshipour0Hossein Namdari1Alireza Koochali2Ulrich Dittmer3Ali Haghighi4Institute of Urban Water Management, RPTU in Kaiserslautern, 67663 Kaiserslautern, GermanyFaculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz 61357-83151, IranGerman Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, GermanyInstitute of Urban Water Management, RPTU in Kaiserslautern, 67663 Kaiserslautern, GermanyInstitute of Urban Water Management, RPTU in Kaiserslautern, 67663 Kaiserslautern, GermanyThis study introduces an innovative ensemble data-driven model designed for short-term water demand forecasting within urban areas. By synergistically combining three distinct machine learning approaches—NHiTS, XGBoost regression, and a multi-head 1D convolutional neural network—our model enhances forecasting accuracy and reliability. This integration not only leverages the unique strengths of each method but also compensates for their individual weaknesses, resulting in a robust solution for predicting urban water demand. Tested against the Battle of Water Demand Forecasting dataset (WDSA-CCWI-2024), our ensemble model demonstrates superior performance, offering a promising tool for efficient water resource management and decision making.https://www.mdpi.com/2673-4591/69/1/69water demand forecastingdeep learningensemble learningtime series analysis |
| spellingShingle | Amin E. Bakhshipour Hossein Namdari Alireza Koochali Ulrich Dittmer Ali Haghighi An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas Engineering Proceedings water demand forecasting deep learning ensemble learning time series analysis |
| title | An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas |
| title_full | An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas |
| title_fullStr | An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas |
| title_full_unstemmed | An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas |
| title_short | An Ensemble Data-Driven Approach for Enhanced Short-Term Water Demand Forecasting in Urban Areas |
| title_sort | ensemble data driven approach for enhanced short term water demand forecasting in urban areas |
| topic | water demand forecasting deep learning ensemble learning time series analysis |
| url | https://www.mdpi.com/2673-4591/69/1/69 |
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