Automated Intelligent Information System for Monitoring and Ensuring the Ecological Safety of Drinking Water

The impact of global challenges on international relations is steadily increasing. One of the most serious problems is ensuring the supply of safe drinking water for the world’s population. Control over water resources plays an important role in interstate relations and has become an increasingly significant factor in modern geopolitical interactions. Within this context, the issue of drinking water availability and safety represents one of the key components of contemporary political and socio-economic relations. The concept of water diplomacy has emerged in the international arena to describe situations where water resources may be used as an instrument of political influence. Similar to many other countries, the Republic of Azerbaijan also faces the problem of water scarcity. Among the most important issues in this area are maintaining the balance of water circulation, substance exchange processes, energy balance, and ecological safety. In this regard, the development of an automated intelligent information system for ensuring the ecological safety of drinking water in the republic is considered appropriate. The proposed system is implemented on a client–server platform. Based on the conceptual and functional models of the problem, the architecture of system objects has been developed. Reservoirs integrated into the network can be centrally controlled from the server–center node during emergency situations, while under normal operating conditions they retain autonomous functionality. The communication routes “object–server–center” and “center–server–object” remain continuously active. The paper provides information on measurement instruments, computational algorithms, and the developed software components. Experimental studies were conducted at the Jeyranbatan Reservoir during 2021–2024 based on 720 measurements obtained from 6–7 monitoring points. The main objective of the research is to ensure operational monitoring of drinking water quality and early detection of potential risks based on long-term measurements of parameters of different origins (physical, chemical, organoleptic, etc.). To verify the adequacy of the automated system, archival data from 2017–2020 were also used. The obtained results demonstrate that the proposed system detects deviations of parameters from normative values with high accuracy (approximately 86–92 %) and effectively supports the decision-making mechanism.