Climate Change Data Portal
DOI | 10.1007/s00704-018-2628-9 |
A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data | |
Tehrany, Mahyat Shafapour1; Jones, Simon1; Shabani, Farzin2,3; Martinez-Alvarez, Francisco4; Dieu Tien Bui5,6 | |
发表日期 | 2019 |
ISSN | 0177-798X |
EISSN | 1434-4483 |
卷号 | 137期号:1-2页码:637-653 |
英文摘要 | A reliable forest fire susceptibility map is a necessity for disaster management and a primary reference source in land use planning. We set out to evaluate the use of the LogitBoost ensemble-based decision tree (LEDT) machine learning method for forest fire susceptibility mapping through a comparative case study at the Lao Cai region of Vietnam. A thorough literature search would indicate the method has not previously been applied to forest fires. Support vector machine (SVM), random forest (RF), and Kernel logistic regression (KLR) were used as benchmarks in the comparative evaluation. A fire inventory database for the study area was constructed based on data of previous forest fire occurrences, and related conditioning factors were generated from a number of sources. Thereafter, forest fire probability indices were computed through each of the four modeling techniques, and performances were compared using the area under the curve (AUC), Kappa index, overall accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). The LEDT model produced the best performance, both on the training and on validation datasets, demonstrating a 92% prediction capability. Its overall superiority over the benchmarking models suggests that it has the potential to be used as an efficient new tool for forest fire susceptibility mapping. Fire prevention is a critical concern for local forestry authorities in tropical Lao Cai region, and based on the evidence of our study, the method has a potential application in forestry conservation management. |
WOS研究方向 | Meteorology & Atmospheric Sciences |
来源期刊 | THEORETICAL AND APPLIED CLIMATOLOGY
![]() |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/90283 |
作者单位 | 1.RMIT Univ, Sch Sci, Geospatial Sci, Melbourne, Vic 3000, Australia; 2.Flinders Univ S Australia, ARC Ctr Excellence Australian Biodivers & Heritag, Global Ecol, Coll Sci & Engn, GPO Box 2100, Adelaide, SA, Australia; 3.Macquarie Univ, Dept Biol Sci, Sydney, NSW, Australia; 4.Pablo de Olavide Univ Seville, Div Comp Sci, Seville, Spain; 5.Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City, Vietnam; 6.Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam |
推荐引用方式 GB/T 7714 | Tehrany, Mahyat Shafapour,Jones, Simon,Shabani, Farzin,et al. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data[J],2019,137(1-2):637-653. |
APA | Tehrany, Mahyat Shafapour,Jones, Simon,Shabani, Farzin,Martinez-Alvarez, Francisco,&Dieu Tien Bui.(2019).A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data.THEORETICAL AND APPLIED CLIMATOLOGY,137(1-2),637-653. |
MLA | Tehrany, Mahyat Shafapour,et al."A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data".THEORETICAL AND APPLIED CLIMATOLOGY 137.1-2(2019):637-653. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。