A Toolbox for Supporting Research on AI in Water Distribution Networks
This work provides a domain-specific tool for AI researchers working on critical infrastructure, but it is incremental as it builds on existing benchmarks and methods without introducing new AI techniques.
The authors tackled the challenge of applying AI to Water Distribution Networks by introducing a Python toolbox that simplifies scenario modeling and generation, enabling AI researchers to access and address problems like leakages and contamination more easily.
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.