机器学习模型因其强大的特征提取能力被广泛应用于滑坡易发性评价，在应用中其算法在不断改进。为解决常见机器学习模型中精度不高的问题，将分组卷积神经网络模型（group convolutional neural network, GCNN）引入滑坡易发性评价，并与多种常见机器学习模型结果进行对比分析，综合评估不同机器学习模型在滑坡易发性评价的适应性。以河北省为研究区，从致灾因子、孕灾环境、承灾体这个3个方面出发，共选取16个影响因子，选择GCNN模型和目前常见的机器学习模型——卷积神经网络模型（convolutional neural network, CNN）、逻辑回归模型（Logistic）、随机森林算法模型（random forest, RF）和支持向量机模型（support vector machine, SVM）构建出相应的易发性评价模型，将研究区划分为4类滑坡易发性区域，并对区划的精确性进行综合评价。研究表明，与其他4种机器学习模型相比，GCNN模型在各混淆矩阵指标下拥有更高评分，更适合进行滑坡易发性区划，得到的滑坡易发区划结果与实际发生滑坡点的一致性较好，划分的滑坡灾害易发区更加准确。

Machine learning models, widely applied in landslide susceptibility assessment due to their powerful feature extraction capabilities, are continuously evolving in their algorithms to address the common issue of low accuracy. The GCNN (group convolutional neural network) model was introduced into landslide susceptibility assessment, and its results were compared with those of various common machine learning models to comprehensively evaluate the adaptability of these models in this field. Taking Hebei Province as the research area, 16 influencing factors were selected from three aspects: triggering factors, pregnant disaster environment, and susceptible bodies. GCNN model and other common machine learning models—CNN (convolutional neural network), Logistic (logistic regression), RF (random forest), and SVM (support vector machine) —were constructed to build corresponding susceptibility assessment models. The research area is divided into four categories of landslide susceptibility zones, and the accuracy of the zoning is comprehensively evaluated. The study indicates that compared with the other four machine learning models, the GCNN model achieves higher scores in various confusion matrix indicators and is more suitable for landslide susceptibility zoning. The resulting zoning of landslide susceptibility is consistent with the actual occurrence of landslide points, indicating a more accurate delineation of landslide-prone areas.