Markov Decision Process Approximation Methods for Water Distribution Network Inspection and Maintenance: A Case Study of the U.S. Virgin Islands
For utilities in resource-constrained settings with limited data, this work provides a decision-support tool that leverages observable system states to infer pipe failures, reducing reliance on extensive sensing.
The paper develops a Markov decision process framework for water distribution network inspection and maintenance, integrating hydraulic simulation to enable decision-making in data-sparse environments. Results show state-dependent optimal policies and virtual sensing capabilities, demonstrating that system-level dynamics can guide maintenance under uncertainty.
We develop a repair-oriented inspection and maintenance decision framework for water distribution networks. This work is motivated by utilities operating in data-sparse environments, such as in remote locations like the U.S. Virgin Islands, where data collection about network state and underground pipeline outages is limited to above-ground and easy to access information (e.g., water tank levels and pump operations). We formulate the problem as a discounted Markov decision process and integrate it with high-fidelity hydraulic simulation. The model captures latent system dynamics without requiring pipe-level sensing. The results reveal state-dependent optimal policies and heterogeneous failure characteristics across pipes, including rare but high-impact behaviors. We further show that certain observable system states uniquely correspond to specific pipe failures, enabling a form of virtual sensing. These findings demonstrate that system-level dynamics can support inspection planning and maintenance decisions under uncertainty in resource-constrained settings.