联合DBSCAN聚类采样和SVM分类的滑坡易发性评价

针对基于机器学习的滑坡易发性评价中非滑坡样本选取不规范导致的分类精度较低问题，本文提出联合基于密度的噪声应用空间聚类（Density-Based Spatial Clustering of Applications with Noise，DBSCAN）采样策略和支持向量机（Support Vector Machine，SVM）分类方法的DBSCAN-SVM滑坡易发性评价模型。首先，基于DBSCAN聚类和空间分析选取非滑坡样本；然后，将样本数据代入SVM分类模型进行训练与验证，预测并提取SVM分类中属于滑坡的概率，获得滑坡易发性；最后，以四川省绵阳市为试验区，预测滑坡易发性概率，基于滑坡易发性精度与分级结果等要素，与传统非滑坡样本采集策略的SVM滑坡易发性评价模型进行对比，并结合实际情况对DBSCAN-SVM模型评价结果进行分析。研究结果表明，相比传统SVM滑坡易发性评价模型，本文提出的DBSCAN-SVM滑坡易发性评价模型在高易发区和极高易发区中包含的滑坡样本数量较多，准确率、召回率、AUC、F1分数均得到提高，精度较高。

Landslide Susceptibility Evaluation Based on Combined DBSCAN Cluster Sampling and SVM Classification

Aiming at the problem of low classification accuracy caused by the non-standard selection of non-landslide samples in the landslide susceptibility evaluation based on machine learning, this paper proposes a combination of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) sampling A DBSCAN-SVM landslide susceptibility evaluation model for strategies and Support Vector Machine (SVM) classification methods. First, non-landslide samples were selected based on DBSCAN clustering and spatial analysis; then the sample data was substituted into the SVM classification model for training and verification, the probability of landslides in the SVM classification was predicted and extracted, and the landslide susceptibility was obtained; Mianyang city is the test area, and the landslide susceptibility probability is predicted. Based on the landslide susceptibility accuracy and classification results, it is compared with the SVM landslide susceptibility evaluation model based on the traditional non-landslide sample collection strategy, and the DBSCAN-SVM is based on the actual situation. The model evaluation results are analyzed. The research results show that, compared with the traditional SVM landslide susceptibility evaluation model, the DBSCAN-SVM landslide susceptibility evaluation model proposed in this paper contains more landslide samples in high-prone areas and extremely high-prone areas, F1 scores are improved, and the accuracy is higher.