ISSN 1003-8035 CN 11-2852/P
    韩俊,王保云. 基于原型网络的云南怒江州泥石流灾害易发性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5): 117-129. DOI: 10.16031/j.cnki.issn.1003-8035.202207023
    引用本文: 韩俊,王保云. 基于原型网络的云南怒江州泥石流灾害易发性评价与区划[J]. 中国地质灾害与防治学报,2023,34(5): 117-129. DOI: 10.16031/j.cnki.issn.1003-8035.202207023
    HAN Jun,WANG Baoyun. A case study on the susceptibility assessment of debris flows disasters based on prototype network in Nujiang Prefecture, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5): 117-129. DOI: 10.16031/j.cnki.issn.1003-8035.202207023
    Citation: HAN Jun,WANG Baoyun. A case study on the susceptibility assessment of debris flows disasters based on prototype network in Nujiang Prefecture, Yunnan Province[J]. The Chinese Journal of Geological Hazard and Control,2023,34(5): 117-129. DOI: 10.16031/j.cnki.issn.1003-8035.202207023

    基于原型网络的云南怒江州泥石流灾害易发性评价与区划

    A case study on the susceptibility assessment of debris flows disasters based on prototype network in Nujiang Prefecture, Yunnan Province

    • 摘要: 针对基于泥石流因子评价方法中选取因子不一及训练样本少的问题,提出了一种基于原型网络的沟谷泥石流灾害易发性评价方法。首先,通过元学习方式组织训练数据,计算每一类沟谷的原型中心。其次,计算未知样本与每一类原型中心的距离,得到其从属类别的概率。最后,根据类别概率计算沟谷的泥石流易发性指数,得到泥石流易发性评价等级。运用模型对怒江州的沟谷进行评价,并与历史灾害数据进行比对,分类正确率达到67.39%,历史事件中泥石流灾害严重程度与模型的评价等级吻合度较好。相比传统实地勘测和因子评价等方法,文章方法能够通过遥感影像进行泥石流灾害区域的快速识别与评价,为泥石流灾害的预警预测研究带来新的思路。

       

      Abstract: In response to the issues of inconsistent factor selection and limited training samples in debris flow factor-based evaluation methods, this study proposed a prototypical network-based approach for assessing the susceptibility of valley debris flow disasters. The method involves organizing the training data through meta-learning and calculating the prototype center for each valley type, serving as a representative of that category. Subsequently, the distance between the features of unknown samples and the prototype center of each class is computed to determine the probability of their classification. Based on the category probabilities, the debris flow susceptibility index of the valley is calculated to obtain the evaluation grade for debris flow susceptibility. The model was applied to evaluate the valleys in Nujiang Prefecture, and its results were compared with historical disaster data, yielding a classification accuracy rate of 67.39%. The evaluation levels provided by the model align well with the severity of debris flow disasters in historical events. Compared to traditional methods such as field surveys and factor evaluation, the method proposed in this paper allows for the rapid identification and evaluation of debris flow disaster areas using remote sensing imagery, presenting new insights for research on early warning and prediction of debris flow disasters.

       

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