A Dataset for Research on Water Sustainability
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911891669909504 |
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| author | Gupta, Pranjol Sen Hossen, Md Rajib Li, Pengfei Ren, Shaolei Islam, Mohammad A. |
| author_facet | Gupta, Pranjol Sen Hossen, Md Rajib Li, Pengfei Ren, Shaolei Islam, Mohammad A. |
| contents | Freshwater scarcity is a global problem that requires collective efforts across all industry sectors. Nevertheless, a lack of access to operational water footprint data bars many applications from exploring optimization opportunities hidden within the temporal and spatial variations. To break this barrier into research in water sustainability, we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it. Our dataset is publicly available at Open Science Framework (OSF) |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17469 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Dataset for Research on Water Sustainability Gupta, Pranjol Sen Hossen, Md Rajib Li, Pengfei Ren, Shaolei Islam, Mohammad A. Machine Learning Artificial Intelligence Computers and Society Performance Freshwater scarcity is a global problem that requires collective efforts across all industry sectors. Nevertheless, a lack of access to operational water footprint data bars many applications from exploring optimization opportunities hidden within the temporal and spatial variations. To break this barrier into research in water sustainability, we build a dataset for operation direct water usage in the cooling systems and indirect water embedded in electricity generation. Our dataset consists of the hourly water efficiency of major U.S. cities and states from 2019 to 2023. We also offer cooling system models that capture the impact of weather on water efficiency. We present a preliminary analysis of our dataset and discuss three potential applications that can benefit from it. Our dataset is publicly available at Open Science Framework (OSF) |
| title | A Dataset for Research on Water Sustainability |
| topic | Machine Learning Artificial Intelligence Computers and Society Performance |
| url | https://arxiv.org/abs/2405.17469 |