A Toolbox for Supporting Research on AI in Water Distribution Networks

The document introduces a Python toolbox called EPyT-Flow, designed to support research on artificial intelligence (AI) in water distribution networks (WDNs) by providing a high-level interface for generating WDN scenario data, access to benchmark datasets for event detection, and an environment for developing control algorithms, aiming to address the challenges faced by WDN operators and facilitate the application of AI in this domain.

Abstract

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.

Method

The authors introduced a Python toolbox called EPyT-Flow, designed to support research on artificial intelligence (AI) in water distribution networks (WDNs). The methodology involved the development of a high-level interface for generating WDN scenario data, providing access to benchmark datasets for event detection, and creating an environment for developing control algorithms. The toolbox aimed to address the challenges faced by WDN operators and facilitate the application of AI in this domain. Additionally, the authors proposed a long-term vision to split the toolbox into three parts: a core part for data generation, a BenchmarkHub for accessing and sharing WDN benchmarks, and a ModelHub for accessing and sharing AI and classic models and algorithms for different tasks in WDNs. The research was supported by the Ministry of Culture and Science NRW (Germany) and the European Research Council (ERC).

Main Finding

The authors introduced a Python toolbox called EPyT-Flow, designed to support research on artificial intelligence (AI) in water distribution networks (WDNs). The methodology involved the development of a high-level interface for generating WDN scenario data, providing access to benchmark datasets for event detection, and creating an environment for developing control algorithms. The toolbox aimed to address the challenges faced by WDN operators and facilitate the application of AI in this domain. Additionally, the authors proposed a long-term vision to split the toolbox into three parts: a core part for data generation, a BenchmarkHub for accessing and sharing WDN benchmarks, and a ModelHub for accessing and sharing AI and classic models and algorithms for different tasks in WDNs. The research was supported by the Ministry of Culture and Science NRW (Germany) and the European Research Council (ERC).

Conclusion

This paper introduces the EPyT-Flow Python toolbox, designed to support AI research in water distribution networks (WDNs) by providing a high-level interface for generating realistic scenario data and accessing benchmark datasets. The toolbox also offers an environment for developing control algorithms and aims to facilitate the application of AI in addressing challenges faced by WDN operators. The authors propose a long-term vision to split the toolbox into three parts: a core part for data generation, a BenchmarkHub for accessing and sharing WDN benchmarks, and a ModelHub for accessing and sharing AI and classic models and algorithms for different tasks in WDNs. The paper emphasizes the importance of easy-to-use toolboxes and access to benchmark datasets for boosting and accelerating research in AI for WDNs, and acknowledges support from the Ministry of Culture and Science NRW (Germany) and the European Research Council (ERC).

Keywords

Water Distribution Networks, AI, Python toolbox, scenario data generation, benchmark datasets, event detection, control algorithms, hydraulic models, water quality, WDN resilience, surrogate models, urban growth, modeling uncertainties, AI-driven methods, Ministry of Culture and Science NRW, European Research Council