The main purpose of this study was to apply the novel bivariate weights-of-evidence-based SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques, namely the naïve Bayes (NB) and Radial basis function networks (RBFNetwork), as benchmark models. Firstly, by using aerial photos and geological field surveys, the 263 landslide locations in the study area were obtained. Next, the identified landslides were randomly classified according to the ratio of 70/30 to construct training data and validation models, respectively. Secondly, based on the landslide inventory map, combined with the geological and geomorphological characteristics of the study area, 14 affecting factors of the landslide were determined. The predictive ability of the selected factors was evaluated using the LSVM model. Using the WoE model, the relationship between landslides and affecting factors was analyzed by positive and negative correlation methods. The above three hybrid models were then used to map landslide susceptibility. Thirdly, the ROC curve and various statistical data (SE, 95% CI and MAE) were used to verify and compare the predictive power of the model. Compared with the other two models, the Sysfor model had a larger area under the curve (AUC) of 0.876 (training dataset) and 0.783 (validation dataset). Finally, by quantitatively comparing the susceptibility values of each pixel, the differences in spatial morphology of landslide susceptibility maps were compared, and the model was found to have limitations and effectiveness. The landslide susceptibility maps obtained by the three models are reasonable, and the landslide susceptibility maps generated by the SysFor model have the highest comprehensive performance. The results obtained in this paper can help local governments in land use planning, disaster reduction and environmental protection.

中文翻译：

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基于GIS的滑坡敏感性模型和空间格局分析人工智能方法的性能评估。

这项研究的主要目的是将基于二元证据权的新型SysFor（SF）应用于滑坡敏感性测绘，以及两种机器学习技术，即朴素贝叶斯（NB）和径向基函数网络（RBFNetwork），作为基准模型。首先，通过航拍照片和地质调查，获得了研究区的263个滑坡位置。接下来，根据70/30的比率对识别出的滑坡进行随机分类，分别构建训练数据和验证模型。其次，根据滑坡清单图，结合研究区的地质和地貌特征，确定了14个影响滑坡的因素。使用LSVM模型评估所选因素的预测能力。使用WoE模型，采用正负相关方法分析了滑坡与影响因素之间的关系。然后将以上三个混合模型用于绘制滑坡敏感性图。第三，使用ROC曲线和各种统计数据（SE，95％CI和MAE）来验证和比较模型的预测能力。与其他两个模型相比，Sysfor模型的曲线下面积（AUC）为0.876（训练数据集）和0.783（验证数据集）。最后，通过定量比较每个像素的磁化率值，比较了滑坡磁化率图的空间形态差异，发现该模型具有局限性和有效性。通过这三个模型获得的滑坡敏感性图是合理的，SysFor模型生成的滑坡敏感性图具有最高的综合性能。本文所获得的结果可以帮助地方政府进行土地利用规划，减灾和环境保护。