Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial reg...

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Bibliographic Details
Main Authors: Zhangwen Su, Haiqing Hu, Mulualem Tigabu, Guangyu Wang, Aicong Zeng, Futao Guo
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Forests
Subjects:
boreal forests
wildfire drivers
geographically weighted regression
geospatial analysis
Online Access:https://www.mdpi.com/1999-4907/10/5/377

Internet

https://www.mdpi.com/1999-4907/10/5/377

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