A Toolbox for Supporting Research on AI in Water Distribution Networks

TL;DR

This paper introduces a Python toolbox designed to facilitate AI research in water distribution networks by providing scenario modeling, event detection benchmarks, and an environment for developing control algorithms to address challenges like leakages and contamination.

Contribution

It presents a comprehensive Python toolbox that simplifies complex scenario generation and benchmarking for AI applications in water distribution networks.

Findings

Enables easy generation of hydraulic and water quality scenarios

Provides access to popular event detection benchmarks

Supports development of control algorithms in water networks

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.