<ul><li>A study on wildfire risk in Iran analyzed the impact of climatic conditions and human activities using remote sensing, GIS, and machine learning.</li><li>Factors like soil moisture, temperature, humidity, population density, and proximity to powerlines were found to influence wildfire susceptibility.</li><li>Human-related factors had a more significant impact on wildfire risk compared to climatic elements, especially during warm and cold seasons.</li><li>The research produced high-resolution wildfire susceptibility maps pinpointing high-risk areas in regions like central Zagros, northeastern Hyrcanian Forest, and northern Arasbaran forest, emphasizing the need for improved fire management strategies.</li></ul>

Assessing wildfire susceptibility in Iran: Leveraging machine learning for geospatial analysis of climatic and anthropogenic factors

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