AUTOMATED MULTI-SENSOR WATER MONITORING AND PREDICTIVE MODELING FOR SUSTAINABLE IRRIGATION MANAGEMENT

Abstract

The sustainable management of water resources in closed lake systems of arid and semi-arid regions remains a critical challenge, particularly due to the high cost of imported monitoring equipment and the lack of automated, localized forecasting solutions [1].

This paper presents a novel automated multi-sensor monitoring and data-driven forecasting system specifically designed for closed water bodies such as endorheic lakes and reservoirs [2].

The system integrates eight key parameters—mineralization (TDS), pH, bacteriological contamination, lake perimeter, humidity, water temperature, dissolved oxygen, and turbidity—measured through a low-cost floating sensor platform and an onshore climate station[3].

Data acquisition, transmission, and preprocessing are fully automated, enabling real-time quality control and feature extraction for seasonal forecasting. Three forecasting models—Multiple Linear Regression (MLR), Random Forest (RF), and Long Short-Term Memory (LSTM)—are implemented and compared against four benchmark methods: conventional laboratory analysis, an imported commercial sensor system, satellite-only estimation, and historical average forecasting [4,5].

The methodology is validated using historical data (2014–2023) from the Aydar Lake system in Uzbekistan [6]. Results indicate that the proposed automated system achieves forecasting accuracy comparable to imported alternatives at approximately one-third of the capital cost, while providing real-time data availability that conventional grab sampling cannot offer [7].

The LSTM model demonstrates the highest predictive performance, with a Nash–Sutcliffe efficiency of 0.91 and root mean square error of 0.28 m during validation [8]. The proposed framework offers a scalable, cost-effective, and automated solution for water resource monitoring and forecasting, particularly suitable for developing countries where access to expensive foreign equipment is limited [9].