The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

中文翻译：

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基于二元统计的人工智能算法在滑坡空间预测中的性能评估与比较

本研究的目的是比较由确定性因子（CFs），证据权重（WoE），证据信念函数（EBF）和两种机器学习模型组成的9个模型，即随机森林（RF）和支持向量机（SVM） ）。第一步，选择15个滑坡条件因子以准备专题图，包括坡度，坡度，高程，水流功率指数（SPI），泥沙输送指数（STI），地形湿度指数（TWI），平面曲率，剖面曲率，土地利用，归一化差异植被指数（NDVI），土壤，岩性，降雨，与河流的距离和与道路的距离。第二步，将152个滑坡以70/30的比例随机分为两组，作为训练和验证数据集。第三步，CF的权重 分别计算条件因子的WoE和EBF模型，并使用权重生成滑坡敏感性图。每个二元模型的权重分别替换为RF和SVM模型，并获得了六个综合模型和滑坡敏感性图。第四步，将接收器工作特性曲线和相关参数用于验证和比较，然后将成功率曲线和预测率曲线进行重新分析。综合结果表明，混合模型优于双变量模型，所有九个模型均具有优异的性能。WoE-RF模型具有最高的预测能力（AUC_T：0.9993，AUC_P：0.8968）。