Robust adaptive optimization for sustainable water demand prediction in water distribution systems
Abstract The advancement of the Internet of Things has positioned intelligent water demand forecasting as a critical component in the quest for sustainable water resource management. Despite the potential benefits, the inherent non-stationarity of water consumption data poses significant hurdles to...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88628-7 |
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| author | Ke Wang Jiayang Meng Zhangquan Wang Kehua Zhao Banteng Liu |
| author_facet | Ke Wang Jiayang Meng Zhangquan Wang Kehua Zhao Banteng Liu |
| author_sort | Ke Wang |
| collection | DOAJ |
| description | Abstract The advancement of the Internet of Things has positioned intelligent water demand forecasting as a critical component in the quest for sustainable water resource management. Despite the potential benefits, the inherent non-stationarity of water consumption data poses significant hurdles to the predictive accuracy of forecasting models. This study introduces a novel approach, the Robust Adaptive Optimization Decomposition (RAOD) strategy, which integrates a deep neural network to address these challenges. The RAOD strategy leverages the Complete Ensemble Empirical Mode Decomposition (CEEMD) to preprocess the water demand series, mitigating the effects of non-stationarity and non-linearity. To further enhance the model’s robustness, an innovative optimization algorithm is incorporated within the CEEMD process to minimize the variance in multi-scale arrangement entropy among the decomposed components, thereby improving the model’s generalization capabilities. The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. To validate the effectiveness of the RAOD strategy, real-world datasets from four distinct geographical regions are employed for multi-step ahead predictions. The experimental outcomes demonstrate that the RAOD model outperforms existing models across all considered metrics, highlighting its suitability for accurate and reliable water demand forecasting in the context of sustainable energy management. |
| format | Article |
| id | doaj-art-8e635ea280ea4733ac14b62acda64429 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8e635ea280ea4733ac14b62acda644292025-02-09T12:29:27ZengNature PortfolioScientific Reports2045-23222025-02-0115111510.1038/s41598-025-88628-7Robust adaptive optimization for sustainable water demand prediction in water distribution systemsKe Wang0Jiayang Meng1Zhangquan Wang2Kehua Zhao3Banteng Liu4College of Information Science and Technology, Zhejiang Shuren UniversitySchool of Microelectronics and Control Engineering, Changzhou UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityCollege of Information Science and Technology, Zhejiang Shuren UniversityAbstract The advancement of the Internet of Things has positioned intelligent water demand forecasting as a critical component in the quest for sustainable water resource management. Despite the potential benefits, the inherent non-stationarity of water consumption data poses significant hurdles to the predictive accuracy of forecasting models. This study introduces a novel approach, the Robust Adaptive Optimization Decomposition (RAOD) strategy, which integrates a deep neural network to address these challenges. The RAOD strategy leverages the Complete Ensemble Empirical Mode Decomposition (CEEMD) to preprocess the water demand series, mitigating the effects of non-stationarity and non-linearity. To further enhance the model’s robustness, an innovative optimization algorithm is incorporated within the CEEMD process to minimize the variance in multi-scale arrangement entropy among the decomposed components, thereby improving the model’s generalization capabilities. The predictive power of the proposed model is harnessed through the construction of deep neural networks that utilize the decomposed data to forecast minutely water demand. To validate the effectiveness of the RAOD strategy, real-world datasets from four distinct geographical regions are employed for multi-step ahead predictions. The experimental outcomes demonstrate that the RAOD model outperforms existing models across all considered metrics, highlighting its suitability for accurate and reliable water demand forecasting in the context of sustainable energy management.https://doi.org/10.1038/s41598-025-88628-7Water demand predictionSequence decompositionPermutation entropySmart water managementLong short-term memory network |
| spellingShingle | Ke Wang Jiayang Meng Zhangquan Wang Kehua Zhao Banteng Liu Robust adaptive optimization for sustainable water demand prediction in water distribution systems Scientific Reports Water demand prediction Sequence decomposition Permutation entropy Smart water management Long short-term memory network |
| title | Robust adaptive optimization for sustainable water demand prediction in water distribution systems |
| title_full | Robust adaptive optimization for sustainable water demand prediction in water distribution systems |
| title_fullStr | Robust adaptive optimization for sustainable water demand prediction in water distribution systems |
| title_full_unstemmed | Robust adaptive optimization for sustainable water demand prediction in water distribution systems |
| title_short | Robust adaptive optimization for sustainable water demand prediction in water distribution systems |
| title_sort | robust adaptive optimization for sustainable water demand prediction in water distribution systems |
| topic | Water demand prediction Sequence decomposition Permutation entropy Smart water management Long short-term memory network |
| url | https://doi.org/10.1038/s41598-025-88628-7 |
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