EFFECTIVE METHODS OF TEACHING GEOGRAPHIC INFORMATION SYSTEMS USING A SUPERCOMPUTER
Keywords:
GIS, artificial intelligence, parallel computing, parallel computing cluster, supercomputer, machine learning, ArcGIS, Python,Abstract
Currently, the growing volume of processing and analysis of large-scale geospatial data requires the use of high-performance computing resources. The application of supercomputers and parallel computing clusters in machine learning, which is a field of artificial intelligence, enables complex modeling, parallel processing of large-scale data, and forecasting. The integration of machine learning methods into geographic information systems (GIS) contributes to the effective solution of tasks such as natural resource management, environmental monitoring, urban planning, and disaster prediction. This article examines the importance of using parallel computing clusters and supercomputers in the development of geographic information systems. During the study, the efficiency of using the PARAMBILIM-2 supercomputer was evaluated using quantitative indicators, and the results showed that the computation time was reduced by several thousand times. These results demonstrate that the use of supercomputers not only increases computational speed but also significantly enhances the efficiency of geospatial analysis and machine learning. As a result, the application of supercomputers and parallel computing clusters in GIS education creates opportunities for innovative applied research and project development. The effectiveness of training was determined by the level of practical skills acquired by students through project-based learning, as well as by the examination grades obtained upon completion of the course.
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