Landslide susceptibility modelling based on AHC-OLID clustering algorithm

Abstract

The aim of this study is to design a novel machine learning model named Agglomerative Hierarchical Clustering algorithm based on Overlapped Interval Divergence distance measure (AHC-OLID), for modelling and assessing landslide susceptibility. The AHC-OLID algorithm is proposed to combat the limitations of many clustering algorithms in modelling and assessing landslide susceptibility, including: pre-defining the number of clusters; sensitivity to the clusters properties and noisy data; as well as difficulty in processing rainfall data. The proposed algorithm addresses these issues by integrating the traditional Agglomerative Hierarchical Clustering (AHC) and Overlapped Interval Divergence distance measure (OLID) methods into landslide susceptibility assessment to enhance its performance. It considers the clusters sizes as well the distances between them and tends to avoid taking into consideration small clusters which are very far from other clusters in the dataset. It is also insensitive to the variation in sizes, variances and shapes of the clusters, which makes the algorithm more advantageous. Besides, the AHC-OLID algorithm makes use of the OLID distance function to process the rainfall data, which takes two factors into consideration: distance between their centers as well as relative size of their overlapped area. Applying this new approach in Baota District, China, produced significant improvement in assessment of landslide susceptibility than previous models. Moreover, the Landslide susceptibility map constructed based on AHC-OLID algorithm can be a useful tool for landslide controlling strategies for proper land use and planning.