An approach to solving the problem of geomagnetic data scarcity in decision-making support

One of the main problems of using data in decision-making support is their scarcity in certain spatial points/areas due to the inability to carry out appropriate measurements. An example is the Earth’s magnetic field data (geomagnetic data) which is used to make decisions to reduce the extreme geophysical events negative impact on objects and systems of the technosphere (power lines, communication systems, railway automation, etc.). An analysis of the existing geomagnetic data collection infrastructure from the standpoint of system analysis made it possible to identify incomplete coverage of the monitoring network, which negatively affects decision-making to ensure technosphere security in the relevant spatial areas. Using the example of geomagnetic data, it was revealed that the known interpolation methods, which do not take into account the features of the spatiotemporal characteristics of the processes described by the data and their dependence on external factors, do not deal effectively with the task. To solve this problem, an approach to adaptive spatial interpolation is proposed the main idea of which is the dynamic selection of interpolation methods that are most effective for various factors. For an example of geomagnetic data two factors were chosen: the affiliation of a spatial point to a certain latitude zone and the index of geomagnetic activity in the time period under consideration. To evaluate the proposed solution, a prototype of a web-based application was developed. The experiment was conducted using geomagnetic information from the SuperMAG project. The proposed approach has proved to be more effective than using any separate interpolation method when comparing the root-mean-square errors. Adaptive interpolation proposed in this paper can be used in systems implementing interpolation of geospatial data, as an alternative to standard interpolation methods, in order to increase the accuracy of data recovery. When working with geomagnetic data, the factors considered in this work (latitudinal zones and geomagnetic activity) can be used, but interpolation of data of a different nature will require preliminary analysis to identify significant factors.

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