MA’LUMOTLARNI AVTOMATLASHTIRISH ASOSIDA SUV RESURSLARINI MONITORING QILISH VA BASHORAT QILISH TIZIMI

Ozodov Ezozbek

Innores · 2026-yil

Annotatsiya

This study address the critical issue of water resourceoverexploitation in Uzbekistan, where utilization levels have reached approximately163% of sustainable capacity, indicating severe hydrologic stress. The researchidentifies key drivers of this imbalance, including inefficient irrigation practices, lackof real-time monitoring systems, and insufficient assessment of the true operationalpotential of water bodies. To overcome these limitations, the paper proposes asintegrated intelligent framework combining multi-sensor measurement systems withadvanced data- driven modeling techniques. The system incorporates both direct andindirect measurement parameters, including hydrological, meteorological andsatellite-derived indicators, unified within and IoT-based real-time monitoringarchitecture.A long short-term memory (LSTM) neural network model is developed tocapture nonline temporal dependencies and forecast water resource dynamics. Themodel demonstrates high predictive accuracy (R2=0.994, MAPE=1.8%) significantlyoutperforming traditional statistical approaches.The results, validated using long-termobservational data from the Aydar-Arnasay lake system, confirm substantial waterdegradation trends and highlight the effectiveness of the proposed approach. Theimplementation of such intelligent monitoring and forecasting systems can enhancewater management efficiency, reduce losses and support data-driven decision-makingunder variability conditions

Maqola ma’lumotlari
MualliflarOzodov Ezozbek
JurnalInnores
Nashr sanasi2026-05-30
Jild2
Son5
Betlar88-100
TilIngliz

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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 system

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