UNIFORM AND UNUNIFORM DISCRETIZATION OF CONTINUOUS INFORMATIONIN THE STUDY OF SOIL EROSION INFORMATION SYSTEM

Abstract

The article considers approaches to discretization of continuous information in the context of the study of soil erosion monitoring information systems. In particular, uniform and non-uniform discretization are considered as two main approaches to data processing that affect the accuracy of the analysis of dynamic processes. It is determined that uniform discretization is effective in cases of stable dynamics of parameter changes, while non-uniform discretization provides an adaptive approach to the analysis of variable conditions characteristic of erosion processes. Special attention is paid to adaptive discretization methods, they allow to increase the accuracy of the assessment of erosion processes due to the optimal distribution of data sampling points. Modern discretization algorithms and their application in geographic information systems are analyzed, and the impact of the choice of method on the quality of forecasting and the efficiency of spatiotemporal data processing is assessed. The proposed approach contributes to increasing the accuracy of soil erosion models, which is of great importance for sustainable land management and the development of strategies to prevent soil cover degradation. It is found that adaptive algorithms based on non-uniform discretization allow for improving the detailing of critical erosion zones and minimizing errors in the analysis of remote sensing data. The possibilities of integrating discretization methods with geographic information systems (GIS), satellite technologies, and erosion process prediction models are also analyzed. It is determined that combined approaches that combine elements of uniform and non-uniform discretization can provide an optimal balance between data processing speed and their accuracy. The results obtained can be used to develop more effective methods for assessing and predicting erosion processes, as well as to improve data processing algorithms in environmental monitoring systems. The proposed approach contributes to increasing the accuracy of soil erosion models, which is important for sustainable land management and the development of strategies to prevent soil degradation.