Machine learning analysis of Iran’s wildfire landscape and anthropogenic influences

Wildfires pose significant challenges globally, including in Iran. This study analyzes wildfire occurrences in Iran from 2001 to 2022, using NASA FIRMS' active fire detections MCD14DL data. To enhance the reliability of this satellite-based dataset, particularly in data-scarce regions like Iran, we applied a multi-sensor cross-validation framework before modeling. We also aim to examine the country's wildfire dynamics over two decades, employing k-means clustering to categorize wildfires into ten clusters, delineating fire zones. Two random forest regression models explore the relationships between annual CO₂ emissions, indicative of human activities, and average temperature, a proxy for climate variability, with wildfire occurrence. Our findings reveal a notable escalation in the frequency and intensity of wildfires across Iran during the study period. Specifically, the western and southwest regions, designated as Zone 05, emerge as highly affected areas, recording 162,734 fires despite their smaller size. The years 2015-2018 stand out as critical, marked by heightened wildfire activity and rapid annual fluctuations. Interestingly, the regression analysis shows a strong correlation between CO₂ emissions and wildfire activity, which highlights the significant influence of human activities. In contrast, the weaker link with the average temperature suggests that climate variability plays a comparatively smaller role in shaping wildfire patterns in Iran during the study period . This study provides insights into Iran's wildfire patterns, revealing that the wildfire regime in Iran is evolving mainly through event frequency rather than fire intensity. These results emphasize the need for stakeholders to understand these dynamics thoroughly for effective mitigation strategies against the environmental and economic challenges posed by wildfires in the region.