考虑到滑坡编录制作的耗时性，建立一种“可迁移”的滑坡易发性模型已越发重要.合理利用现有完整滑坡数据地区的样本集对无样本区域进行易发性预测具有重要意义.运用迁移成分分析（transfer component analysis，TCA）方法，结合深度学习卷积神经网络（convolutional neural network，CNN），尝试引入一种基于迁移学习域自适应方法的TCA-CNN模型，并以福建省两个库岸地区为例，提取11个库岸相关环境因子建立滑坡空间数据库，将有样本的池潭库区易发性模型迁移至无样本的棉花滩库区进行预测，实现跨区域滑坡易发性评价.通过对棉花滩库区进行易发性预测，结果显示：（1）采用TCA方法处理后的不同研究区数据最大均值差异（maximize mean discrepancy，MMD）明显降低（0.022），数据实现近似同分布；（2）TCA-CNN模型的跨区域预测精度为0.854，高于CNN模型（0.791），且通过历史滑坡验证其落入高、极高易发性区间的滑坡频率比占比最高（89.1%）；（3）受试者工作特性（receiver operating characteristic，ROC）曲线下面积TCA-CNN模型为0.93，高于CNN模型的0.90.可见TCA-CNN模型能够有效运用建模区的样本数据实现对无样本区域的易发性评价，且相比于传统机器模型在进行跨区域预测时具有更高、更稳定的预测准确率，具备更强的泛化能力.

It is crucial to create a “migratable” landslide susceptibility model considering the time-consuming process of recording landslides. However, a sample set of all known landslide data areas must be used in forecasting the susceptibility of unsampled regions adequately. In this paper, we attempt to develop a TCA-CNN model to enable trans-regional landslide susceptibility evaluation, which is based on the adaption domain of transfer learning by integrating the transfer component analysis (TCA) with deep learning convolutional neural network (CNN). The spatial database of landslides is constructed by extracting 11 environmental factors of the reservoir bank area, then the Mianhuatan reservoir area without samples is then predicted using the susceptibility model from the Chitan reservoir area with samples. The results show that: (1) The maximum mean discrepancy (MMD) of data from different study areas treated by TCA decreased significantly (0.022), and the data is approximately identically distributed; (2) The trans-regional prediction accuracy by the TCA-CNN model is 0.854, which is higher than that of the CNN model (0.791). And it can be verified that the proportion of landslide frequency falling into the high/extremely-high susceptibility interval is the highest (89.1%) in the historical landslide; (3) The area under the receiver operating characteristic (ROC) curve of the TCA-CNN model is 0.93, which is higher than that of the CNN model (0.90). It’s obvious that the TCA-CNN model can effectively use the samples data of the modeling area to realize the susceptibility evaluation of the unsampled area. Compared with the traditional machine model, TCA-CNN model has higher and more stable prediction accuracy and stronger generalization ability in cross-region prediction.