Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal

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.