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calendar15 декабр 2025
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GIS, MASHINAVIY O‘QITISH VA CHUQUR O‘QITISH YORDAMIDA RELYEF HAMDA SHAMOL TA’SIRI OSTIDA PURKAGICH (SPRINKLER) SUV TAQSIMOTINI MODELLASHTIRISH

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MAQOLA ANNOTATSIYASI

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Sprinkler sug‘orish tizimlarida suvning teng taqsimlanmasli-gi sug‘orish samaradorligini pasaytiruvchi asosiy omillardan biri bo‘lib, bunda shamolning o‘zgaruvchanligi va yer nishabligining turlichaligi muhim rol o‘ynay- di. Ushbu tadqiqotda mazkur muammoni hal qilish maqsadida GIS (geoinformat-sion tizimlar), mashinaviy o‘qitish (ML) va chuqur o‘qitish (DL) texnologiyalari integratsiyasi asosida aqlli yondashuv ishlab chiqildi. Tadqiqotda Slope (nishab-lik), Aspect (yo‘nalish), shamol tezligi va yo‘nalishi, evapotranspiratsiya (ET) va NDVI kabi parametrlar asosida zonalar “quruq”, “normal” yoki “botqoqlik” ho-latiga ajratildi. Random Forest modeli yordamida dastlabki klassifikatsiyada 47,5 % aniqlikka erishildi. Keyingi bosqichda 1D konvolyutsion neyron tarmoq (CNN) modeli qo‘llanib, bu model har bir zonani 6 ta parametr asosida o‘rgandi. CNN modeli 50 epoch davomida 80 %dan ortiq o‘quv aniqligi va 75 %ga yaqin tekshiruv aniqligini qayd etdi. Ushbu yondashuv O‘zbekiston va Markaziy Osiyo sharoitida iqlim o‘zgarishi va murakkab relyef sharoitida aniq hamda optimal-lashtirilgan sprinkler sug‘orish tizimini yaratish uchun kuchli asos bo‘lib xizmat qiladi.

MUALIFLAR

Teglar

# water distribution# slope# Machine Learning# deep learning# mashinaviy o‘qitish# NDVI# уклон# GIS.# chuqur o‘qitish# shamol ta’siri# CNN# Sprinkler system# wind drift# sprinkler tizimi# nishablik# suv taqsimoti# konvolyutsion neyron tarmoq (KNT# система дождевания# снос капель ветром# распределение воды# нормализованный разностный вегет# свёрточная нейронная сеть (CNN)# машинное обучение (Machine Lear# глубокое обучение (Deep Learning# ГИС (GIS).

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