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• RISK‑ORIENTED GEOPORTAL DECISION SUPPORT SYSTEM FOR TERRITORIALLY DISTRIBUTED ORGANIZATIONAL SYSTEMS

  А.М. Bershadsky , S.А. Yamashkin

  278-297

  2026-07-07

  Abstract ▼

  The article discusses the development of a risk-oriented geoportal decision support system for territorially distributed organizational systems (TDOS). The aim of the work is to develop an architecture and a formalized model that integrates spatial data, risk structures, key performance indicators (KPIs), and management action options within a unified analytical framework. The relevance is due to the fact that classical DSS and geoportals fragmentarily cover the tasks of TDOS management due to the lack of integration of spatial analysis with risk cascading models, which leads to inconsistency of decisions and increased territorial vulnerability. The methodological foundation includes the formalization of "object-risk-indicator-impact" relationships, the construction of directed influence graphs, and mechanisms for the propagation of risk effects across the territory and management levels. A multi-layered architecture of the geoportal platform is proposed, including subsystems for spatial data collection, risk analytics, KPI dashboards, and scenario modeling. The technical implementation is based on open geoportals of the Russian Geographical Society and a unified spatial data repository. As a pilot area, the regional management system for development and natural resource use of the Republic of Mordovia was studied, where a prototype of the risk-oriented module was implemented. The article demonstrates the ability to visualize the distribution of natural and man-made risks, assess integral territorial vulnerability indices, and select priority management scenarios. The results of implementation at EM-KAT LLC showed a reduction in energy consumption. The application of the system in the Main Directorate of the Ministry of Emergency Situations for the Republic of Mordovia made it possible to optimize territorial management and increase risk predictability. The proposed approach ensures holistic and reproducible management of territorially distributed systems, increasing their resilience to local and transitive impacts, decision-making transparency, and the efficiency of interdepartmental coordination

• OPTIMIZATION OF THE COMPUTATIONAL SCHEME FOR THE INTERPOLATION OF DECADAL METEOROLOGICAL DATA BY INVERSE DISTANCE WEIGHTING WITH PARALLEL PROCESSING OF MULTIPLE TIME SLICES

  О.М. Golozubov , А.V. Kozlovskiy , E.V. Melnik , Y.E. Melnik , А.N. Samoylov

  22-32

  2025-12-30

  Abstract ▼

  The present study is devoted to solving the problem of computational inefficiency in spatial interpolation of large arrays of decadal meteorological data using the inverse distance weighting method. Traditional approaches involving sequential and independent processing of each time slice demonstrate a linear increase in execution time and significant RAM consumption, which becomes a critical barrier to the rapid construction of detailed and geographically linked raster fields in GeoTIFF format. This significantly limits the use of the method in tasks requiring rapid processing of long-term data archives. The purpose of this work is to develop and validate an optimized computational scheme that can radically reduce time costs while maintaining the completeness and accuracy of the results. The key scientific novelty of the proposed approach lies in the fundamental rethinking of the computational process. Instead of repeating identical operations many times, a scheme is proposed based on a single calculation of the full vector of geodetic distances from each grid cell to all weather stations. This most resource-intensive operation is performed only once. Subsequently, the resulting distance vector is applied to all time slices (decades) to calculate the interpolated values, which eliminates the main computational redundancy and ensures a sublinear dependence of processing time on the number of decades. To further improve performance, a parallel processing mechanism is used at the CPU level, implemented by dynamically dividing the raster into independent computing units (batches). The size of the batches is adaptively adjusted taking into account the available RAM, which guarantees the stability and scalability of the solution on systems of various capacities. The testing of the method on real meteorological data for the period 2015-2024 demonstrated a radical reduction in the execution time. In particular, processing ten decade time slices on a standard laptop takes less than 3.5 minutes, and on a server platform it takes about 3 minutes, which represents a multiple acceleration compared to traditional implementations. Thus, the developed solution makes the operational processing of large spatial and temporal meteorological arrays a reality for a wide range of researchers, opening up new opportunities for climate monitoring, agrometeorology and geoinformation analysis without the need for specialized expensive equipment