Source term inversion coupling Kernel Principal Component Analysis, Whale Optimization Algorithm, and Backpropagation Neural Networks (KPCA-WOA-BPNN) for complex dispersion scenarios

Abstract

Accurate and rapid source term estimation is critical for consequence assessment and emergency decision-making in nuclear accidents. Neural network methods provide a promising approach to achieving this goal, but they are mainly demonstrated in ideal scenarios with flat terrain. In this study, a source term inversion model combining the Kernel Principal Component Analysis, the Whale Optimization Algorithm, and the Backpropagation Neural Networks (KPCA-WOA-BPNN) was proposed for source term inversion in a more realistic scenario with complex terrain. The method was validated against two simulated release scenarios based on the meteorology and topography of a real Chinese nuclear power plant. A comprehensive sensitivity analysis of the position distribution, quantity, and quality of measurement sites and the four input parameters was conducted. The results revealed that the relative error of the KPCA-WOA-BPNN was below 2% for both scenarios. The sites within 1.2 km of the release point are critical for the performance. Even with only 6 sites, the error of the method can be as low as 3.82%. The method is insensitive to the disturbances in wind speed, mix layer height, and release height, but less robust to those in concentration. The method also works with other optimization algorithms and presents low error levels, indicating good extendibility.