Model for Long-Term Monitoring of Geological Processes in Kazakhstan Using Intelligent Drone Ports

Abstract

The creation of autonomous systems for geomonitoring using unmanned aerial vehicles (UAV) and smart drone ports provide new possibilities for problem solving in geology, particularly in remote and geodynamically active areas of Kazakhstan. During the study, a multi-criteria mathematical model was created to compare drones with and without artificial intelligence (AI). The model contains ten standardized indicators of performance (autonomy, route planning, productivity, risk management, predicting geo-anomalies, scalability, etc.) with relative weights defined by experts. Analysis of the results revealed the high efficiency of the AI-oriented approach in processing time, spatial analysis, autonomy and scalability. The comparative assessment framework consists of 10 evaluation criteria weighted, namely autonomous management, route optimization, data processing efficiency, GIS integration, geoanomaly forecasting, resistance to external risks, operational efficiency, maintenance costs, data acquisition speed, and potential scalability. As a result, scientifically justified recommendations were made, and research areas were identified, such as the development of trend forecasting AI, the migration of drone ports to distributed mission planning, and the creation of a digital ecosystem of geomonitoring. The proposed framework offers a solid base of moving toward scalable, intelligent, as well as data-driven geomonitoring systems. The development of AI-powered drone-port infrastructure should play a crucial role in improving geological safety, facilitating timely hazard identification, and ensuring sustainable land-use planning in remote and geodynamically active areas.



Received: 12 December 2025 | Revised: 27 July 2026 | Accepted: 16 September 2026



Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.



Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.



Author Contribution Statement

Gani Baiseitov: Conceptualization, Methodology, Validation, Writing – original draft, Supervision, Project administration, Funding acquisition. Alexey Semchenko: Formal analysis, Resources, Writing – original draft. Daulet Toibazarov: Software, Investigation, Resources, Data curation. Praveen Kumar: Software, Validation, Writing – original draft, Visualization. Zarina Kutpanova: Formal analysis, Writing – review & editing.