MA’LUMOTLARNI AVTOMATLASHTIRISH ASOSIDA SUV RESURSLARINI MONITORING QILISH VA BASHORAT QILISH TIZIMI
Keywords:
Keywords: water resource management, hydrological monitoring, multi-sensor systems, internet of things (IoT), real-time data acquisition, LSTM neural networks, time series forecasting, water resource degradation, remote sensing analysis intelligent decision support systemAbstract
This study address the critical issue of water resource
overexploitation in Uzbekistan, where utilization levels have reached approximately
163% of sustainable capacity, indicating severe hydrologic stress. The research
identifies key drivers of this imbalance, including inefficient irrigation practices, lack
of real-time monitoring systems, and insufficient assessment of the true operational
potential of water bodies. To overcome these limitations, the paper proposes as
integrated intelligent framework combining multi-sensor measurement systems with
advanced data- driven modeling techniques. The system incorporates both direct and
indirect measurement parameters, including hydrological, meteorological and
satellite-derived indicators, unified within and IoT-based real-time monitoring
architecture.
A long short-term memory (LSTM) neural network model is developed to
capture nonline temporal dependencies and forecast water resource dynamics. The
model demonstrates high predictive accuracy (R2=0.994, MAPE=1.8%) significantly
outperforming traditional statistical approaches.The results, validated using long-term
observational data from the Aydar-Arnasay lake system, confirm substantial water
degradation trends and highlight the effectiveness of the proposed approach. The
implementation of such intelligent monitoring and forecasting systems can enhance
water management efficiency, reduce losses and support data-driven decision-making
under variability conditions
