Identifying Landslide Hotspots Using Unsupervised Clustering: A Case Study

Authors

DOI:

https://doi.org/10.62411/faith.3048-3719-37

Keywords:

Algorithms, Clustering, Landslide, Mean, Mean Shift, Metrics, Topographic data, Unsupervised Machine Learning

Abstract

Landslides pose significant threats to life, property, and infrastructure. This study explores applying unsupervised learning techniques to identify and understand landslide-prone areas. We analyzed topographic data by employing K-Means, Hierarchical Clustering, Spectral Clustering, Mean Shift Clustering, and DBSCAN to uncover hidden patterns in landslide occurrence. Evaluation metrics, including the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used to assess the performance of these algorithms. Hierarchical Clustering achieved the highest Silhouette Score of 0.635, indicating excellent cluster separation. However, Mean Shift Clustering outperformed the other methods with a superior Davies-Bouldin Index of 0.603 and the highest Calinski-Harabasz Index of 4121.75, demonstrating the best overall clustering performance. DBSCAN also performed well, with a Silhouette Score of 0.610 and 12 noise points identified. These findings contribute to a deeper understanding of landslide spatial distribution and can inform the development of effective early warning systems and mitigation strategies.

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Author Biographies

Ikechukwu Daniel, Nnamdi Azikiwe University

Department of Geological Science, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Anambra Sate, Nigeria

Lateef Akinyemi, University of South Africa

Centre for Augmented  Intelligence and Data Science, School of Computing, CSET, University of South Africa, Johannesburg, South Africa

Department of Electronic and Computer Engineering, Faculty of Engineering, Lagos State University, Epe, Lagos, Nigeria

Obianuju Udekwu, Nnamdi Azikiwe University

Department of Geological Science, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Anambra Sate, Nigeria

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2024-11-06

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[1]
I. Daniel, L. Akinyemi, and O. Udekwu, “Identifying Landslide Hotspots Using Unsupervised Clustering: A Case Study”, J. Fut. Artif. Intell. Tech., vol. 1, no. 3, pp. 249–268, Nov. 2024.

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