INNOVATIVE MACHINE LEARNING SOLUTIONS FOR PREDICTING THE ELEVATION OF GEOGRAPHICAL POINTS

Abstract

This study comprehensively examines contemporary issues in predicting topographic elevation through the integration of Geographic Information Systems (GIS) and Machine Learning algorithms. Currently, the development of high-precision Digital Elevation Models (DEM) for the territory of Kazakhstan is of paramount importance. This is primarily due to the unique geomorphological complexity of the country; the Kazakh landscape, ranging from the Caspian depressions to the high-altitude systems of the Tien Shan and Altai, poses a significantly challenge for any mathematical model.

From a practical standpoint, such natural heterogeneity significantly reduces the effectiveness of classical geostatistical interpolation methods, such as Kriging or Inverse Distance Weighting (IDW). Standard approaches often fail to account for sharp topographic gradients, leading to substantial errors. To address these shortcomings, this research employs modern data-driven approaches. The subject of the study is the process of regression forecasting of elevation points by integrating spatial data from GIS platforms into machine learning models.          

The primary objective is to identify the most optimal model adapted to the specific characteristics of the local terrain. To this end, three different architectures were tested: Random Forest, Gradient Boosting, and Artificial Neural Networks (ANN). In the initial stage, GIS data underwent preprocessing and normalizations, after which the predictive capacity of each model was compared against actual measurement points.

The results indicated that machine learning methods provide 20-35% higher accuracy compared to traditional techniques. During the comparative analysis, the Random Forest model datasets. This model allowed for a more accurate description of local terrain fluctuations.

In conclusion, the proposed methodology paves the way for elevating digital cartography in Kazakhstan to a qualitatively new level. It is recommended to implement these research findings in geodetic design, hydrological modeling, environmental monitoring, and state systems for transformation aimed at the efficient use of the country`s land resources.