Sentence examples for morphometric attributes from from inspiring English sources

We have assessed dam stability using 6 separate indices (with a total of 10 stability thresholds) based on watershed and dam morphometric attributes (from: Canuti et al. 1998; Casagli and Ermini 1999; Ermini and Casagli 2003; Korup 2004; Tacconi Stefanelli et al. 2015, 2016, 2017).

We used four morphometric attributes extracted from a 37.5-m resolution DEM as well as two non-morphometric attributes observed in the neighborhood.

The Classification Tree (CT) method was applied to assess whether lynchets and undulations can be discriminated through morphometric attributes (slope, curvature, profile curvature and planform curvature) and soil thickness (CTsoil) or through morphometric attributes only (CTtopo).

MPS uses information at multiple cell locations including morphometric attributes at a target mapping cell, i.e. digital elevation model (DEM) derivatives, and non-morphometric attributes, i.e. landforms at the neighboring cells, to determine the landform.

It also determined the relationship among their morphometric attributes and the factors of gully development in the study area.

The models developed by the CART and FDA revealed that in addition to soil thickness, the morphometric attributes slope and profile curvature significantly influence the spatial distribution of landscape structures.

This work aimed to evaluate whether different types of landscape structures (undulations, lynchets and undisturbed surfaces) can be discriminated by their morphometric attributes and the soil thickness.

The results achieved in this study proposes that morphometric attributes defining basin geometry as well as shape, length of stream, stream network topology and topography dissection can be well retrieved from ASTER-DEM and accomplished to generate data on stream number and basin relief.

We derived morphometric attribute values for the stability analyses from Natural Resource Canada digital elevation data and LiDAR data in a GIS.

Remove the "hidden" attribute from the folder.

We demonstrate that machine learning algorithms can be used to predict the methylation profile of a patient from morphometric features extracted from IHC images of cancer tissues.