Abstract
In this paper, we clustered and analyzed landslides and investigated their underlying driving forces at two levels, country and cluster, all over Iran. Considering 12 conditioning factors, the landslides were clustered into nine relatively homogeneous regions using the Contextual Neural Gas (CNG) algorithm. Next, their underlying driving forces were ranked using the Random Forest (RF) algorithm at country and cluster levels. Our results indicate that the mechanisms for landslide occurrence varied for each cluster and that driving forces of the landslides operated differently at a country level compared to the cluster level. Moreover, slope, altitude, average annual rainfall, and distance to the main roads were identified as the most important causes of landslides within all clusters. Thus, for effective management and modelling landslides on a large scale, the variation in the functionality of effective factors should be considered.
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Communicated by: H. A. Babaie
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Shafizadeh-Moghadam, H., Minaei, M., Shahabi, H. et al. Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran. Earth Sci Inform 12, 1–17 (2019). https://doi.org/10.1007/s12145-018-0354-6
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DOI: https://doi.org/10.1007/s12145-018-0354-6