AUTOMATION-ORIENTED METHODOLOGY FOR MULTI-PARAMETER DATA ACQUISITION AND FORECASTING IN CLOSED HYDROLOGICAL SYSTEMS BASED ON INTEGRATED SENSOR NETWORKS

Abstract

This paper presents a novel automation-oriented methodology for real-time data acquisition and forecasting of water resources in closed hydrological systems under stochastic environmental conditions. The study is positioned within the domain of automation of technological processes and addresses the lack of integrated control-oriented monitoring systems in water resource management.

The proposed approach is based on the development of a multi-level automated data acquisition system, combining distributed sensor networks, perimeter-based environmental measurements, and centralized analytical processing. The system architecture includes buoy-based measurement nodes equipped with ultrasonic level sensors, turbidity sensors, total dissolved solids (TDS), pH, and oxidation-reduction potential (ORP) sensors, integrated with external climatic and geoinformation data sources.

A key methodological contribution is the introduction of a perimeter moisture monitoring model, implemented via a custom-designed soil probe system. Unlike conventional point-based hydrological measurements, this approach enables continuous assessment of boundary conditions, including infiltration, evaporation dynamics, and shoreline displacement. These parameters are incorporated into a unified state-space representation of the reservoir.

The data acquisition process is fully automated and synchronized through an IoT-based communication framework, enabling continuous streaming, preprocessing, and storage of multi-source data. The collected dataset is further utilized within a predictive analytical module, allowing early-stage forecasting of water balance variations and resource availability.

The integration of technological process automation with a novel multi-parameter measurement methodology significantly improves system observability and forecasting accuracy. The results obtained for Aydarkul Lake demonstrate that the proposed system enhances decision-support capabilities for rational water use and mitigation of water deficit risks in closed reservoir environments.