In this article, we propose and test alternative sampling strategies based on clustering distribution concepts to increase the efficiency of the landslide susceptibility model outcomes, instead of common random selection method for training and testing samples. To that end, we prepared a comprehensive landslide inventory and used six unsupervised clustering algorithms (K-means, K-medoids, hierarchical cluster (HC) analysis, expectation–maximization using Gaussian mixture models (EM/GMM), affinity propagation, and mini batch K-means) to generate six different training datasets. After getting the cluster pattern in each technique, we classified it into 70% and 30% for training and testing samples, respectively. We generated an additional training dataset using random selection procedure to test the hypothesis. The EM/GMM model exhibited the highest accuracy than the other methods. The findings confirm the hypothesis and recommend investing in natural distribution of landslides incident, as training concepts, instead of random sampling.

中文翻译：

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尼泊尔加德满都谷地北部滑坡敏感性绘图的空间聚类和建模

在本文中，我们提出并测试基于聚类分布概念的替代抽样策略，以提高滑坡敏感性模型结果的效率，而不是训练和测试样本的常见随机选择方法。为此，我们准备了一份全面的滑坡清单，并使用了六种无监督聚类算法（K-means、K-medoids、层次聚类 (HC) 分析、使用高斯混合模型 (EM/GMM) 的期望最大化、亲和传播和迷你批量 K 均值）以生成六个不同的训练数据集。在获得每种技术的聚类模式后，我们将其分为 70% 和 30%，分别用于训练和测试样本。我们使用随机选择程序生成了一个额外的训练数据集来测试假设。EM/GMM 模型比其他方法表现出最高的准确度。研究结果证实了这一假设，并建议投资于滑坡事件的自然分布，作为培训概念，而不是随机抽样。