Cluster analysis applied to engineering geological mapping

Trefolini, Emanuele; Rindinella, Andrea; Disperati, Leonardo

doi:10.3301/rol.2015.40

Cited by 1 publication

(2 citation statements)

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“…The reference map SA1-U (Fig. 2) has been obtained by means of unsupervised clustering (ISODATA algorithm), as in Trefolini et al (2015), by choosing 15 clusters to describe the morphometric space. Thereafter, each morphometric cluster have been assigned to depth classes by analyzing sampling distribution of SD depth measurements, collected in SA1.…”

Section: Sdmentioning

confidence: 99%

“…The aim of this study is to compare the accuracy of SD depth maps obtained through cluster analysis methods, either by using SD depth measurements specifically acquired within the study area (Trefolini et al, 2015), or by exploiting the same kind of data previously collected within regions located far away from the study area, although characterized by similar geological properties. To this aim, two study areas situated in the Northern Apennines ( Fig.…”

Section: Introductionmentioning

confidence: 99%

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Mapping slope deposits depth by means of cluster analysis: a comparative assessment

Venturini

Trefolini

Patelli³

et al. 2016

ROL

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In this work a comparison among slope deposits (SD) maps obtained by integrating field measurements of SD depth and cluster analysis of morphometric data has been performed. Three SD depth maps have been obtained for the same area (SA1) by using different approaches. Two maps have been achieved by implementing both the supervised and unsupervised approaches and exploiting the dataset of SD depths previously collected in a region (SA2) characterized by the same bedrock lithology, although located 35 km far from the SA1. The results have been validated against a reference map based on SD depth measurements acquired during this work within the SA1 and mapped by unsupervised clustering. The outcome of the study shows the feasibility of the methodology proposed to obtain depth maps of SD. Nevertheless the very low map accuracy suggests that relationships among main morphometric variables and slope deposits depth are not constant at regional scale, although considering areas characterized by the same bedrock lithology. Hence, maps of SD depth should be based on depth data specifically acquired within the area under study. In order to improve the exploitation of SD depth datasets outside their provenance area, further research are necessary on clustering algorithms performance as well as additional morphometric and environmental variables to be employed in spatial analysis.

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Section: Sdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%