Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture
Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand com...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Water Research X |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589914724000598 |
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| author | Qidong Que Jinliang Gao Yizhou Qian |
| author_facet | Qidong Que Jinliang Gao Yizhou Qian |
| author_sort | Qidong Que |
| collection | DOAJ |
| description | Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5 %-20 % per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain. |
| format | Article |
| id | doaj-art-d869b2e445eb443a843109e37f293f20 |
| institution | Kabale University |
| issn | 2589-9147 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Water Research X |
| spelling | doaj-art-d869b2e445eb443a843109e37f293f202024-11-22T07:38:44ZengElsevierWater Research X2589-91472024-12-0125100269Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architectureQidong Que0Jinliang Gao1Yizhou Qian2School of Environment, Harbin Institute of Technology, Harbin 150090, China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, ChinaSchool of Environment, Harbin Institute of Technology, Harbin 150090, China; State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, ChinaSchool of Environment, Tsinghua University, Beijing 100084, China; Corresponding author.Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5 %-20 % per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.http://www.sciencedirect.com/science/article/pii/S2589914724000598Short-term water demand forecastingConvolutional Neural NetworkAttention mechanismLong-short term memory neural networkInterpretability in deep learning |
| spellingShingle | Qidong Que Jinliang Gao Yizhou Qian Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture Water Research X Short-term water demand forecasting Convolutional Neural Network Attention mechanism Long-short term memory neural network Interpretability in deep learning |
| title | Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture |
| title_full | Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture |
| title_fullStr | Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture |
| title_full_unstemmed | Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture |
| title_short | Water demand forecasting in multiple district metered areas based on a multi-scale correction module neural network architecture |
| title_sort | water demand forecasting in multiple district metered areas based on a multi scale correction module neural network architecture |
| topic | Short-term water demand forecasting Convolutional Neural Network Attention mechanism Long-short term memory neural network Interpretability in deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589914724000598 |
| work_keys_str_mv | AT qidongque waterdemandforecastinginmultipledistrictmeteredareasbasedonamultiscalecorrectionmoduleneuralnetworkarchitecture AT jinlianggao waterdemandforecastinginmultipledistrictmeteredareasbasedonamultiscalecorrectionmoduleneuralnetworkarchitecture AT yizhouqian waterdemandforecastinginmultipledistrictmeteredareasbasedonamultiscalecorrectionmoduleneuralnetworkarchitecture |