Due to the progress in the development of computing system performance and the advancement of the Internet, an increasing number of areas of human activity are being reflected in databases and geographic information systems (GIS). Since water is a vital factor for human existence, numerous databases and GIS systems are related to water consumption, monitoring of water sources, and studying the dynamics of natural and artificial reservoirs.Today, digitalization of water resources is an important strategic task for monitoring water resources of our country (Kazakhstan). Our team is engaged in developing a GIS system dedicated to monitoring the condition of dams in the Republic of Kazakhstan. The database and web services that provide the system's functionality and interface were developed. The system offers functionality for various types of monitoring, analysis, and statistics collection on dams, as well as working with the dam passport. In the future, the project will be developed and scaled up to expand the analysis instruments and reporting functions, as well as to enhance the use of machine learning. Performed the task of searching for potential dams based on cartographic images using Artificial Neural Networks (ANN). The section dealing with potential dams is titled “Moderation.” The system has different user types. They perform specific roles that are integral to the application's functionality. The dam monitoring section is a complex system that comprises several monitoring types. Application developed using microservices architecture (REST API)—instruments: GIS-related APIs, Golang programming language, PostgreSQL DBMS, Fiber, Kubernetes, Docker. The project has its server for data processing. The domain ‘ kazdams.kz’ is reserved for system operations. The development of a GIS system for digitalization, automation, and data processing of the dam is a necessary and significant task in our project. The research is funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan, IRN AP19675038.
Tanyrbergenova et al. (2026) studied this question.