Samer Ahmed

Reliable anomaly detection in water distribution networks requires diverse and well-structured data covering both normal and faulty operating conditions. In this project, the student will design and implement an automated simulation and data-generation pipeline that produces time-series data (e.g. pressure, flow, demand) for water networks under a variety of scenarios, including leaks and sensor malfunctions. The work focuses on wrapping and orchestrating existing simulation tools (such as EPANET/WNTR), systematically varying configurations and fault parameters, and organising outputs into reproducible, machine-learning-ready datasets. The project is well suited for Bachelor or Master students with a background in computer science, data science, or AI, who are comfortable programming in Python and willing to independently learn domain-specific tools through documentation and examples. Prior knowledge of water networks is not required. References:
DiTEC-WDN Dataset DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks LeakG3PD: A Python Generator and Simulated Water Distribution System Dataset EPANET WNTR