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首页> 外文期刊>Theoretical and applied climatology >A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data
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A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data

机译：使用LogitBoost机器学习分类器和多源地理空间数据的热带森林火灾敏感性空间预测的新型集成建模方法

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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.
机译：可靠的森林火灾敏感性地图是进行灾害管理的必要条件，也是土地使用规划的主要参考来源。我们着手通过在越南老街地区进行的比较案例研究，评估基于LogitBoost集成决策树（LEDT）机器学习方法在森林火灾敏感性制图中的应用。全面的文献搜索将表明该方法以前尚未应用于森林大火。支持向量机（SVM），随机森林（RF）和内核逻辑回归（KLR）被用作比较评估的基准。根据先前森林火灾发生的数据构建了研究区域的火灾清单数据库，并从许多来源生成了相关的调节因素。此后，通过四种建模技术中的每一种来计算森林火灾概率指数，并使用曲线下面积（AUC），Kappa指数，总体准确性，特异性，敏感性，阳性预测值（PPV）和阴性预测值比较性能值（NPV）。 LEDT模型在训练和验证数据集上均表现出最佳性能，证明了92％的预测能力。与基准模型相比，它的整体优势表明它有潜力用作森林火灾敏感性制图的有效新工具。防火是热带老蔡地区当地林业部门的关键问题，根据我们的研究证据，该方法在林业保护管理中具有潜在的应用价值。

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    《Theoretical and applied climatology》 |2019年第2期|637-653|共17页
  • 作者

    Tehrany Mahyat Shafapour; Jones Simon; Shabani Farzin; Martinez-Alvarez Francisco; Dieu Tien Bui;

  • 作者单位

    RMIT Univ, Sch Sci, Geospatial Sci, Melbourne, Vic 3000, Australia;

    RMIT Univ, Sch Sci, Geospatial Sci, Melbourne, Vic 3000, Australia;

    Flinders Univ S Australia, ARC Ctr Excellence Australian Biodivers & Heritag, Global Ecol, Coll Sci & Engn, GPO Box 2100, Adelaide, SA, Australia|Macquarie Univ, Dept Biol Sci, Sydney, NSW, Australia;

    Pablo de Olavide Univ Seville, Div Comp Sci, Seville, Spain;

    Ton Duc Thang Univ, Geog Informat Sci Res Grp, Ho Chi Minh City, Vietnam|Ton Duc Thang Univ, Fac Environm & Labour Safety, Ho Chi Minh City, Vietnam;

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