为提高地震滑坡易发性评价精度,从多模态数据融合角度提出一种新的地震滑坡易发性评价模型。以芦山地震为例,从多源数据中提取15种滑坡影响因子,将这些影响因子按4种不同类型的模态数据进行融合;以斜坡单元为评价单元,利用多模态分类模型评价地震滑坡易发性,并与逻辑回归模型比较;最后分别采用基于受试者工作特征曲线和区划滑坡灾害点密度2种精度评价方法对评价结果进行检验。结果表明,基于多模态分类模型的地震滑坡易发性评价结果具有更高的精度,其评价结果的曲线下面积、高易发区-极高易发区的滑坡灾害点密度分别为0.86、2.24个/km2,均优于逻辑回归模型,表明该模型在区域内具有较好的适用性。研究结果从数据融合角度为提高地震滑坡易发性评价精度提供了新思路。
Abstract
To improve the accuracy of assessing seismic landslide susceptibility, we proposed a landslide susceptibility assessment model from the perspective of multimodal data fusion. With the Lushan earthquake as a case study, we extracted fifteen causative factors of landslide from multi-source data and divided them into four modalities. With the slope unit being the assessment unit, we compared and evaluated the present method and the logistic regression model by using receiver operating characteristic (ROC) curve and zonal landslide hazard point density. Results demonstrate that the area under the ROC curve is 0.86, and the hazard point density of high and very high susceptibility zones is 2.24 points/km2, indicating that the present model is well applicable to the study region. Also, the present model has higher accuracy and better performance than the logistic regression model, suggesting that the presented model has a better applicability in the region.
关键词
滑坡 /
易发性评价 /
精度 /
多模态分类模型 /
影响因子 /
芦山地震
Key words
landslide /
susceptibility assessment /
accuracy /
multimodal classification model /
causative factors /
Lushan earthquake
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基金
国家重点研发计划项目(2018YFC15035);湖南省研究生科研创新项目(CX20200223);中南大学创新驱动计划项目(2020CX036)
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