Leakage Localization Algorithm for Buried Gas Pipeline Networks with a Fusion of Dimensionless Theory and Smart Optimization

Accurate localization of leaks in buried gas pipeline networks is pivotal for urban safety and environmental protection. While previous studies have developed dimensionless gas diffusion models for simulating concentration distributions, their potential for leak source inversion and localization remains underutilized. This study proposes an improved leakage localization algorithm integrating a dimensionless diffusion model with a multistrategy improved particle swarm optimization (PSO) framework, termed GPPSO, which is developed by enhancing standard PSO with two key strategies: (1) good point set (GPS)-based initialization to boost global search ability and population diversity; and (2) a periodic oscillation mutation mechanism to mitigate premature convergence and enhance stability. Validated using publicly available high-pressure pipeline leakage experimental data and medium- to low-pressure pipeline network numerical simulation data (with the numerical model verified against experimental results), the algorithm enables leak localization using sensor concentration data, with errors decreasing progressively as monitoring data accumulate. For the medium- to low-pressure simulation scenarios, the proposed GPPSO-based method reduces localization errors by 77.8% and 66.7% compared with ant colony optimization and standard PSO, respectively. Overall, the proposed algorithm achieves significantly higher localization accuracy and lower total errors, verifying its feasibility and providing a reliable and scalable solution for intelligent pipeline leak monitoring.