Information system for radiological contamination assessment based on incomplete data from an irregular observation network
DOI:
https://doi.org/10.34121/1028-9763-2026-3-49-59Keywords:
radiation mapping, evolutionary algorithm, inverse problem, incomplete data, irregular observation network, information system, decision support system, Shelter ObjectAbstract
An approach is proposed for constructing a spatial radiation contamination map of complex industrial facilities in cases where detailed site survey is limited or hazardous due to high dose rates. Under such conditions, available equivalent dose rate measurements are obtained only at individually accessible points, forming an irregular observation network. The problem is treated as an inverse problem of reconstructing the spatial activity distribution from a limited number of dosimetric measurements, allowing for explicit consideration of the physical constraints of the model and the facility geometry. An information system that covers all major stages of dosimetric data processing — from data loading and initial approximation to iterative activity distribution reconstruction and result validation against control points — has been developed. The functional structure is described using IDEF0 (five processing stages with feedback loops). The architecture is implemented as a multi-tier system consisting of a web tier (Nginx, Gunicorn, Django), a computational node (Python, NumPy, Pandas), and a data storage tier (PostgreSQL), interacting via the RabbitMQ message broker. Decoupling the computational core from the web tier allows the system to operate either as a standalone decision support system or as a computational module within other information systems. Validation has been performed on experimental data from the Shelter Object of the Chornobyl NPP. After more than 7,000 iterations of the evolutionary algorithm, a mean absolute percentage error of less than 23 % has been achieved at a relative measurement uncertainty of approximately 17 %, corresponding to the accuracy limit imposed by the input measurement error. Figs.: 5. Refs.: 23 titles.
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