A Toolbox for Supporting Research on AI in Water Distribution Networks
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910470734086144 |
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| author | Artelt, André Kyriakou, Marios S. Vrachimis, Stelios G. Eliades, Demetrios G. Hammer, Barbara Polycarpou, Marios M. |
| author_facet | Artelt, André Kyriakou, Marios S. Vrachimis, Stelios G. Eliades, Demetrios G. Hammer, Barbara Polycarpou, Marios M. |
| contents | Drinking water is a vital resource for humanity, and thus, Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling \& generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_02078 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Toolbox for Supporting Research on AI in Water Distribution Networks Artelt, André Kyriakou, Marios S. Vrachimis, Stelios G. Eliades, Demetrios G. Hammer, Barbara Polycarpou, Marios M. Artificial Intelligence Computational Engineering, Finance, and Science Systems and Control Drinking water is a vital resource for humanity, and thus, Water Distribution Networks (WDNs) are considered critical infrastructures in modern societies. The operation of WDNs is subject to diverse challenges such as water leakages and contamination, cyber/physical attacks, high energy consumption during pump operation, etc. With model-based methods reaching their limits due to various uncertainty sources, AI methods offer promising solutions to those challenges. In this work, we introduce a Python toolbox for complex scenario modeling \& generation such that AI researchers can easily access challenging problems from the drinking water domain. Besides providing a high-level interface for the easy generation of hydraulic and water quality scenario data, it also provides easy access to popular event detection benchmarks and an environment for developing control algorithms. |
| title | A Toolbox for Supporting Research on AI in Water Distribution Networks |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Systems and Control |
| url | https://arxiv.org/abs/2406.02078 |