气候变化领域集成服务门户(CCPortal): Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case

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DOI	10.1007/s11676-024-01723-9
	Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case
	Eker, Remzi; Alkis, Kamber Can; Aydin, Abdurrahim
发表日期	2024
ISSN	1007-662X
EISSN	1993-0607
起始页码	35
结束页码	1
卷号	35 期号:1
英文摘要	Disturbances such as forest fires, intense winds, and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics, with contributions from climate change. Consequently, there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies. While susceptibility assessment using machine learning methods has increased, most studies have focused on a single disturbance. Moreover, there has been limited exploration of the use of Automated Machine Learning (AutoML) in the literature. In this study, susceptibility assessment for multiple forest disturbances (fires, insect damage, and wind damage) was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate (RFD) in Turkey. The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC (area under the curve) values. The extra tree classifier (ET) algorithm was selected for modeling the susceptibility of each disturbance due to its good performance (AUC values > 0.98). The study evaluated susceptibilities for both individual and multiple disturbances, creating a total of four susceptibility maps using fifteen driving factors in the assessment. According to the results, 82.5% of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels. Additionally, a potential forest disturbances map was created, revealing that 15.6% of forested areas in the Izmir RFD may experience no damage from the disturbances considered, while 54.2% could face damage from all three disturbances. The SHAP (Shapley Additive exPlanations) methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.
英文关键词	AutoML; Forest disturbances; Forest fire; Insect; Susceptibility; Wind
语种	英语
WOS研究方向	Forestry
WOS类目	Forestry
WOS记录号	WOS:001197259300001
来源期刊	JOURNAL OF FORESTRY RESEARCH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/300968
作者单位	Izmir Katip Celebi University; Izmir Katip Celebi University; Duzce University
推荐引用方式 GB/T 7714	Eker, Remzi,Alkis, Kamber Can,Aydin, Abdurrahim. Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case[J],2024,35(1).
APA	Eker, Remzi,Alkis, Kamber Can,&Aydin, Abdurrahim.(2024).Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case.JOURNAL OF FORESTRY RESEARCH,35(1).
MLA	Eker, Remzi,et al."Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case".JOURNAL OF FORESTRY RESEARCH 35.1(2024).