Metageosystem Analysis Based on a System of Machine Learning and Simulation Algorithms

Metageosystem Analysis Based on a System of Machine Learning and Simulation Algorithms

© 2022 by IJETT Journal
Volume-70 Issue-12
Year of Publication : 2022
Author : Stanislav Yamashkin, Anatoliy Yamashkin, Milan Radovanović, Marko Petrović, Ekaterina Yamashkina
DOI : 10.14445/22315381/IJETT-V70I12P201

How to Cite?

Stanislav Yamashkin, Anatoliy Yamashkin, Milan Radovanović, Marko Petrović, Ekaterina Yamashkina, "Metageosystem Analysis Based on a System of Machine Learning and Simulation Algorithms," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 1-12, 2022. Crossref,

The study presented in this article aims to solve the scientific problem of increasing the efficiency of using modeling and machine learning models in solving problems of analysis and classification of metageosystems. The article describes an approach aimed at improving the efficiency of machine learning models in solving the problem of classifying metageosystems, which makes it possible to overcome the limitations imposed on the use of convolutional neural network models. The article is also devoted to solving the scientific problem of multifaceted quantitative analysis of intercomponent links in metageosystems of different hierarchical levels based on simulation modeling. It is proved that the study of the structure and properties of metageosystems should be based on the analysis of complex properties and patterns of interaction of territorial systems distributed in space. A set of requirements for the framework for creating simulation models of spatial processes is formulated, and an algorithm for developing a simulation model that describes the spatio-temporal processes occurring in complex territorial systems is presented. The study also showed that combining models into an ensemble based on the proposed metaclassifier architecture makes it possible to increase the stability of the analyzing system: the accuracy of decisions made by the ensemble tends to tend to the accuracy of the most efficient monoclassifier of the system. Systematic analysis of territory descriptors integrated based on data from different sources significantly increases the accuracy of metageosystem classification.

Geosystem approach, Metageosystems, Earth remote sensing data, Classification, Machine learning, Simulation.

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