This study proposes a source term inversion method for nuclear accidents based on the Harris Hawks Optimization (HHO) algorithm and a Gaussian plume model, enabling accurate estimation of radionuclide release rates and the two-dimensional location of release points using off-site monitoring data under accident scenarios. To evaluate model performance, validation was conducted through simulated experiments under two accident scenarios with known and unknown release locations and tracer experiments involving seven different release scenarios. The simulation results demonstrate that, compared with two other swarm intelligence algorithms, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), the HHO-based inversion model achieves higher estimation accuracy, faster convergence speed, and greater stability during iterative inversion. The convergence rate and accuracy of the model are somewhat dependent on the initialization range of the population and the boundary constraints of the target parameters. The tracer experiment validation shows that the HHO model performs well in most cases, with an average relative error of 0.0341 in release rate inversion and an average positional deviation of 133 m across the seven experiments. Sensitivity analysis indicates that the HHO inversion model exhibits certain robustness in estimating release rates, while the two-dimensional location of the release point is more susceptible to interference from noise in off-site monitoring data.
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