CLUSTERING OF EUROPEAN COUNTRIES BASED ON THEIR AVERAGE TEMPERATURE AND HUMIDITY TO FIND THE BEST CONDITIONS OF SUSTAINABLE PLANTING BY USING PYTHON

Authors: Vladyslav Khoroshun, Dmytro Yarmoliuk, Tariq Eldakruri & Edip Senyurek

ABSTRACT

Analysis of climatic conditions for growing agricultural crops in most European countries, taking into account humidity and temperature values for 1975-2024. The analysis is based on open climate databases from the Copernicus E-OBS website [1]. Vector maps of the Earth’s surface from the Natural Earth website [2] were used to preserve the exact borders of European countries. In order to distribute the data into similar climatic zones, the K-Means clustering method was applied in the Python (PyCharm) programming environment, which allowed eight clusters to be identified. During data processing, Python libraries such as xarray, pandas, geopandas, numpy, tqdm, os, sklearn. cluster, matplotlib. pyplot, and zipfile were used, which made it possible to efficiently process large amounts of data. The arithmetic mean formula was used to calculate the average yearly values of temperature and humidity. A quantitative assessment of average temperature and humidity over the last 50 years shows that the average temperature increase across Europe of approximately 1.9 oC while humidity in some regions has decreased by 8.6%. The results demonstrate clear climate zones in Europe, which reflect important factors in agricultural production and clearly show climate change and global warming. The data obtained can be the basis for further research aimed at forecasting climate changes, developing strategic decisions for agricultural development, and creating relevant applications with elements of artificial intelligence.

Index Terms— climate change, agriculture, temperature, humidity, Python, planting, sustainability.

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