Enhancing accuracy and efficiency in calibration of drinking water distribution networks through evolutionary artificial neural networks and expert systems

ABSTRACT The calibration of drinking water distribution network (DWDN) models is essential to ensure accurate simulation, efficient operation, and informed decision-making. As DWDNs evolve due to seasonal changes, shifting demands, or infrastructure updates, maintaining model accuracy over time becomes increasingly important. However, limited measurement availability and high model complexity make calibration a persistent challenge. To address this, ES-NEAT is introduced, an automatic calibration methodology that combines expert systems (ES) with neuro-evolution of augmenting topologies (NEAT). The method integrates expert knowledge with neural network evolution to efficiently solve high-dimensional calibration problems. ES-NEAT achieves high accuracy under sparse data conditions while keeping computational costs moderate. It also stores calibration knowledge in a structured format, enabling faster and more consistent recalibration over time. This adaptability supports long-term model reliability. The methodology was validated on a benchmark network and a real DWDN in Flanders, Belgium, demonstrating robust performance, efficient convergence, and generalizability across calibration scenarios.