2020
DOI: 10.1016/j.jafrearsci.2019.103707
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Statistical approach of factors controlling drainage network patterns in arid areas. Application to the Eastern Anti Atlas (Morocco)

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Cited by 16 publications
(14 citation statements)
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References 50 publications
(62 reference statements)
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“…Principal components furnish macro parameters, i.e., synthetic data that convey strong and significant information and, therefore, are particularly suitable and relevant for time monitoring or digital mapping and spatial analysis, just like original parameters [14,[23][24][25]. The scores of the samples along the principal components were used for the mapping of the main sources of variation in water quality identified in the study.…”
Section: Mappingmentioning
confidence: 99%
“…Principal components furnish macro parameters, i.e., synthetic data that convey strong and significant information and, therefore, are particularly suitable and relevant for time monitoring or digital mapping and spatial analysis, just like original parameters [14,[23][24][25]. The scores of the samples along the principal components were used for the mapping of the main sources of variation in water quality identified in the study.…”
Section: Mappingmentioning
confidence: 99%
“…On the other hand, the dendritic, pinnate, parallel, trellis, and rectangular patterns are patterns of low relief, that is to say of a weakly eroded zone associated with a weak tectonic activity, as is the case in the Central Anti Atlas [14]. The correlation between PC1 and the geological parameters "Line-Dens" and "Rock-type" is linked to the local structural control, highlighted by the presence of drainage network patterns (rectangular, trellis, herringbone, and barbed) whose development is mainly controlled by geological structuring [4,5,7,19,23,43].…”
Section: Drainage Pattern Relief and Geological Parametersmentioning
confidence: 99%
“…They are orthogonal to each other and therefore represent independent sources of variability, i.e., independent associated processes. Taking into account the main PCs makes it possible to concentrate the information in a reduced number of factorial axis while losing a minimum of the information contained in the dataset, which constitutes a dimensional reduction of the data hyper-space [7,9]. To determine the impact of climate differences on the factors that control the variability in drainage network patterns, PCA was first applied to the entire dataset (318 basins) and then individually to each sector.…”
Section: Multivariate Analysismentioning
confidence: 99%
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