Comparing machine learning algorithms to predict vegetation fire detections in Pakistan

作者：Fahad Shahzad, Kaleem Mehmood, Khadim Hussain, Ijlal Haidar, Shoaib Ahmad Anees, Sultan Muhammad, Jamshid Ali, Muhammad Adnan, Zhichao Wang, Zhongke Feng · 发表于：Fire Ecology · 年份：2024 · DOI：10.1186/s42408-024-00289-5 · 被引用次数：46 · 研究领域：Fire effects on ecosystems、Fire Detection and Safety Systems、Remote Sensing in Agriculture

Abstract Vegetation fires have major impacts on the ecosystem and present a significant threat to human life. Vegetation fires consists of forest fires, cropland fires, and other vegetation fires in this study. Currently, there is a limited amount of research on the long-term prediction of vegetation fires in Pakistan. The exact effect of every factor on the frequency of vegetation fires remains unclear when using standard analysis. This research utilized the high proficiency of machine learning algorithms to combine data from several sources, including the MODIS Global Fire Atlas dataset, topographic, climatic conditions, and different vegetation types acquired between 2001 and 2022. We tested many algorithms and ultimately chose four models for formal data processing. Their selection was based on their performance metrics, such as accuracy, computational efficiency, and preliminary test results. The model’s logistic regression, a random forest, a support vector machine, and an eXtreme Gradient Boosting were used to identify and select the nine key factors of forest and cropland fires and, in the case of other vegetation, seven key factors that cause a fire in Pakistan. The findings indicated that the vegetation fire prediction models achieved prediction accuracies ranging from 78.7 to 87.5% for forest fires, 70.4 to 84.0% for cropland fires, and 66.6 to 83.1% for other vegetation. Additionally, the area under the curve (AUC) values ranged from 83.6 to 93.4% in forest fires,...