基于自组织特征映射网络-随机森林模型的滑坡易发性评价

为深入探讨评价单元和非滑坡样本选取对滑坡易发性预测的影响，构建了一种基于自组织特征映射网络-随机森林模型的滑坡易发性评价模型。该模型针对栅格单元和斜坡单元在滑坡易发性评价中的不足，结合栅格单元和斜坡单元的相互关系，提出了滑坡易发性指数的优化计算方法。在此基础上，基于随机森林Tree Bagger分类器构建滑坡易发性评价模型，通过对比分析自组织特征映射网络和随机方法选取非滑坡样本对评价结果的影响，探讨自组织特征映射网络、随机森林和自组织特征映射网络-随机森林三种评价模型的有效性；将评价模型应用于大余县滑坡易发性评价。结果显示，随机森林模型和自组织特征映射网络-随机森林模型的预测精度较高，分别达到91.19%和94.94%，成功率曲线的AUC值分别为0.822和0.849，表明自组织特征映射网络-随机森林模型具有更高的预测率和成功率, 自组织特征映射网络聚类的预测精度虽然有限，但作为非滑坡样本的选择方法，能够有效提高随机森林模型的评价精度。

Evaluation on landslide susceptibility based on self-organizing feature map network and random forest model:A case study of Dayu County of Jiangxi Province

In order to further explore the influence of evaluation units and non-landslide sample selection methods on landslide susceptibility prediction, a landslide susceptibility evaluation model is established based on self-organizing feature map network and random forest model in this paper. According to the relationship between grid units and slope units, an optimized calculation method of landslide susceptibility index is proposed. Aiming at the deficiencies of grid units and slope units in the evaluation of landslide susceptibility, this model proposes an optimized calculation method for landslide susceptibility index based on the relationship between grid cells and slope cells. On this basis, a landslide susceptibility evaluation model was established based on the random forest Tree Bagger classifier. By comparing and analyzing the influence of self-organizing feature map network and random non-landslide sample selection methods on the evaluation results, the effectiveness of the three evaluation models of self-organizing feature map network, random forest and self-organizing feature map network-random forest were discussed. The evaluation model has been applied to the landslide susceptibility evaluation in Dayu County. The results show that the prediction accuracy of random forest and self-organizing feature map network-random forest is higher,reaching 91.19% and 94.94% respectively, and the AUC of success rate curve was 0.822 and 0.849 respectively. It shows that self-organizing feature map network-random forest has higher prediction rate and success rate,although the prediction accuracy of self-organizing feature map network clustering is limited, it can effectively improve the evaluation accuracy of random forest model as the basis for selecting non landslide samples.