【摘要】 机器学习方法是进行滑坡易发性分析常用的研究方法之一。在进行滑坡样本编录工作时，常会面临因客观条件限制导致数据量不足的问题，从而影响滑坡易发性评价模型的性能。为此，本研究结合全国尺度的滑坡灾害数据集，采用学习率预热-余弦退火(Warmup-Cosine Annealing,WCA)优化策略，提出了一种基于迁移学习的滑坡易发性评价方法。将该模型应用于重庆地区的滑坡易发性分析中，结果表明：相较于传统深度学习模型，当分别使用目标域1%、5%和10%的数据进行再训练时，模型的AUC值分别提高了51.00%、24.40%和2.15%，模型损失分别减少了16.12%、27.61%和15.44%。更多还原

【Abstract】 Machine-learning methodologies have increasingly been embraced in landslide susceptibility assessment. However, the considerable time and financial burdens of landslide inventories often result in persistent data scarcity, which frequently impedes the generation of accurate and informative landslide susceptibility maps. Addressing this challenge, this study compiled a nationwide dataset and developed a transfer learning-based model to evaluate landslide susceptibility in the Chongqing region specifically. Notably, the proposed model, calibrated with the warmup-cosine annealing(WCA) learning rate strategy, demonstrated remarkable predictive capabilities, particularly in scenarios marked by data limitations and when training data were normalized using parameters from the source region. This is evidenced by the area under the receiver operating characteristic curve(AUC) values, which exhibited significant improvements of 51.00%, 24.40% and 2.15%, respectively, compared to a deep learning model, in contexts where only 1%, 5% and 10% of data from the target region were used for retraining. Simultaneously, there were reductions in loss of 16.12%, 27.61% and 15.44%, respectively, in these instances.更多还原