IOT VA SUN’IY INTELLEKT INTEGRATSIYASI ORQALI SUV RESURSLARINING SAMARALI BOSHQARUVI
Keywords:
IoT, sun’iy intellekt, suv resurslarini boshqarish, raqamli monitoring, aqlli tizimlar, ekologik xavfsizlik.Abstract
Suv resurslarini samarali boshqarish bugungi global iqlim o‘zgarishlari, demografik o‘sish va sanoatlashtirish sharoitida dolzarb muammolardan biri hisoblanadi. Ushbu maqolada zamonaviy axborot texnologiyalari, xususan, Internet of Things va sun’iy intellekt vositalarining integratsiyasi orqali suv resurslarini boshqarish tizimlarini takomillashtirish masalalari yoritiladi. Tadqiqotda IoT qurilmalari yordamida ma’lumotlar yig‘ilishi va ularni sun’iy intellekt algoritmlari yordamida tahlil qilish orqali qaror qabul qilish jarayoni tahlil qilinadi. Shuningdek, real vaqtli monitoring, prognozlash va avtomatik boshqaruv mexanizmlarining imkoniyatlari ko‘rib chiqiladi. O‘zbekiston sharoitida bu texnologiyalarni joriy etish istiqbollari va amaliy yechimlar ham muhokama qilinadi.
References
1. Ahmed A., Rauf H.T., Alghamdi A., et al. IoT-based Smart Water Quality Monitoring System. IEEE Access.
2. Gubbi J., Buyya R., Marusic S., Palaniswami M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems.
3. Liu Y., Wu G., Yang S. Smart water grid: the future water management platform. Water Resources Management.
4. Zhou K., Fu C., Yang S. Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews
5. Khalilov R.X. IoT texnologiyalarining ekologiya sohasidagi qo‘llanilishi. TATU Ilmiy Axborotnomasi.
6. Mahmud K., Town G.E. A Review of IoT-Based Smart Water Management Systems. Sensors.
7. Zhou D., Li Y. Artificial Intelligence Applications in Water Resource Management. Journal of Environmental Informatics.
8. O‘zbekiston Respublikasi Suv xo‘jaligi vazirligi rasmiy nashrlari va tahliliy hisobotlari.
9. Xasanovich, S. E. (2023). Neural Network Model of Energy Saving of Combined Drum Dryer. Texas Journal of Engineering and Technology, 20, 45-50. URL: https://zienjournals.com/index.php/tjet/article/view/4060
10. Xasanovich, S. E. (2023). Neural Network Model of Sunflower Seed Drying Process in Combined Drum Dryer. Eurasian Journal of Engineering and Technology, 18, 45-49. URL:https://www.geniusjournals.org/index.php/ejet/article/view/4211
