Using ensemble systems to study natural processes

Abstract

Abstract Increasing accuracy of the data analysis of remote sensing of the Earth significantly affects the quality of decisions taken in the field of environmental management. The article describes the methodology for decoding multispectral space images based on the ensemble learning concept, which can effectively solve important problems of geosystems mapping, including diagnostics of the structure and condition of catchment basins, inventory of water bodies and assessment of their ecological state, study of channel processes; monitoring and forecasting of functioning, dynamics and development of geotechnical systems. The developed methodology is based on an algorithm for analyzing the structure of geosystems using ensemble systems based on a fundamentally new organization of the metaclassifier that allows for a weighted decision based on the efficiency matrix, which is characterized by an increase in accuracy of the decoding of space images and resistance to errors. The metaclassification training algorithm based on the method of weighted voting of monoclassifiers is proposed, in which the weights are calculated on the basis of error matrix metrics. The methodology was tested at the test site ‘Inerka’. The performed experiments confirmed that the use of ensemble systems increases the final accuracy, objectivity, and reliability of the analysis.