Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method
Abstract The growing global demand for water and energy has created an urgent necessity for precise forecasting and management of these resources, especially in urban regions where population growth and economic development are intensifying consumption. Shenzhen, a rapidly expanding megacity in Chin...
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Nature Portfolio
2024-12-01
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| Online Access: | https://doi.org/10.1038/s41598-024-81836-7 |
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| author | Dianli Wang Yu Zhang Nasser Yousefi |
| author_facet | Dianli Wang Yu Zhang Nasser Yousefi |
| author_sort | Dianli Wang |
| collection | DOAJ |
| description | Abstract The growing global demand for water and energy has created an urgent necessity for precise forecasting and management of these resources, especially in urban regions where population growth and economic development are intensifying consumption. Shenzhen, a rapidly expanding megacity in China, exemplifies this trend, with its water and energy requirements anticipated to rise further in the upcoming years. This research proposes an innovative Convolutional Neural Network (CNN) technique for forecasting water and energy consumption in Shenzhen, considering the intricate interactions among climate, socio-economic, and demographic elements. The proposed approach integrates a CNN model with an Enhanced Gorilla Troops Optimization (EGTO) algorithm to demonstrate superior performance compared to other leading methods in terms of accuracy and reliability. The results show a strong correlation between the simulated and observed data, with a correlation coefficient of 0.87 for water consumption and 0.91 for energy consumption, indicating a high level of agreement between the simulated and real-world data. Also, it is indicated that the new technique can accurately forecast water and energy consumption, achieving a mean absolute error (MAE) of 0.63 and a root mean square error (RMSE) of 0.58, respectively. The research indicates that the suggested approach can promote policymakers and stakeholders in making well-informed decisions by delivering precise predictions of water and energy usage. This, in turn, can facilitate better resource distribution, minimize waste, and promote greater sustainability. The study emphasizes the necessity of incorporating climate change and socio-economic factors into the forecasting process and showcases the proposed method’s potential to aid decision-making in this domain. |
| format | Article |
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| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-b6e7061ba43b4a11b74a2aa2be889d852025-08-20T02:43:33ZengNature PortfolioScientific Reports2045-23222024-12-0114111810.1038/s41598-024-81836-7Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning methodDianli Wang0Yu Zhang1Nasser Yousefi2School of Intelligent Manufacturing, Changchun Sci-Tech UniversityInternational College, Krirk University, Khet Bang KhenScience and Research Branch, Islamic Azad UniversityAbstract The growing global demand for water and energy has created an urgent necessity for precise forecasting and management of these resources, especially in urban regions where population growth and economic development are intensifying consumption. Shenzhen, a rapidly expanding megacity in China, exemplifies this trend, with its water and energy requirements anticipated to rise further in the upcoming years. This research proposes an innovative Convolutional Neural Network (CNN) technique for forecasting water and energy consumption in Shenzhen, considering the intricate interactions among climate, socio-economic, and demographic elements. The proposed approach integrates a CNN model with an Enhanced Gorilla Troops Optimization (EGTO) algorithm to demonstrate superior performance compared to other leading methods in terms of accuracy and reliability. The results show a strong correlation between the simulated and observed data, with a correlation coefficient of 0.87 for water consumption and 0.91 for energy consumption, indicating a high level of agreement between the simulated and real-world data. Also, it is indicated that the new technique can accurately forecast water and energy consumption, achieving a mean absolute error (MAE) of 0.63 and a root mean square error (RMSE) of 0.58, respectively. The research indicates that the suggested approach can promote policymakers and stakeholders in making well-informed decisions by delivering precise predictions of water and energy usage. This, in turn, can facilitate better resource distribution, minimize waste, and promote greater sustainability. The study emphasizes the necessity of incorporating climate change and socio-economic factors into the forecasting process and showcases the proposed method’s potential to aid decision-making in this domain.https://doi.org/10.1038/s41598-024-81836-7Convolutional Neural Network (CNN)Urban water-energy consumptionOptimizationClimate changesSustainable urban development |
| spellingShingle | Dianli Wang Yu Zhang Nasser Yousefi Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method Scientific Reports Convolutional Neural Network (CNN) Urban water-energy consumption Optimization Climate changes Sustainable urban development |
| title | Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method |
| title_full | Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method |
| title_fullStr | Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method |
| title_full_unstemmed | Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method |
| title_short | Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method |
| title_sort | urban water energy consumption prediction influenced by climate change utilizing an innovative deep learning method |
| topic | Convolutional Neural Network (CNN) Urban water-energy consumption Optimization Climate changes Sustainable urban development |
| url | https://doi.org/10.1038/s41598-024-81836-7 |
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