Forecasting the Environmental Health Index of Uzbekistan Regions using Machine Learning and Artificial Intelligence Methods

Ravshanov, N., Pambudi, Achmad Tirta Dharu Wahyu, Safari, Muhammad, Kamoliddinova, F., Равшанов, Н., Памбуди, Ахмад Тирта Дхару Вахью, Сафари, Мухаммад, Камолиддинова, Ф.

Ҳисоблаш ва амалий математика муаммолари · 2026-yil

Annotatsiya

Recent years have witnessed significant climatic changes and increasing environmental pressure globally, including in Uzbekistan, necessitating an objective regional assessment. This research develops an approach for forecasting the Environmental Health Index (EHI) by integrating statistical analysis and machine learning algorithms. Long-term meteorological data from 13 regions (temperature, humidity, wind speed) were normalized to construct the EHI, with the Random Forest regressor applied to model nonlinear dependencies. Model performance was validated using MAE, MSE, and ????2 metrics. For the first time, an EHI based on climatic factors was developed specifically for Uzbekistan using a data-driven approach with automated feature weighting. Results demonstrate a stable correlation between climate variables and environmental status, suggesting potential degradation in certain regions if current trends persist.

Maqola ma’lumotlari
MualliflarRavshanov, N., Pambudi, Achmad Tirta Dharu Wahyu, Safari, Muhammad, Kamoliddinova, F., Равшанов, Н., Памбуди, Ахмад Тирта Дхару Вахью, Сафари, Мухаммад, Камолиддинова, Ф.
JurnalҲисоблаш ва амалий математика муаммолари
Nashr sanasi2026-05-02
Son2
Betlar42-60
TilRus
DOI10.71310/pcam.2_72.2026.04

Kalit so‘zlar

machine learning, Random Forest, ecological forecasting, climate factors, regional analysis, sustainable development, машинное обучение, Random Forest, экологическое прогнозирование, климатические факторы, региональный анализ, устойчивое развитие

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