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Dergiler / Manas Journal of Engineering / 2019 / Cilt: 7 - Sayı: 2

Calculating slope gradient variations in the submarine landforms by R and Python statistical libraries

Polina LEMENKOVA
Sayfa
99–113
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Programming codes and snippets are presented for repeatability of the methods in similar research tasks.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Multiple factors affect submarine geomorphology causing variations in the gradient slope: geological settings (rock composition, structure, permeability, erodibility of the materials), submarine erosion, gravity flows of water streams, tectonics, sediments from the volcanic arcs, transported by transverse submarine canyons.
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Temel Bulgular & Veri:The research workflow can be summarized in five steps: 1) Partial least squares regression analysis; 2) Violin plots, modified box plot approach; 3) Modelling variations of depth and slope gradient, facetted in multi-panel plots by 4 tectonic plates; 4) Calculating normalized steepness angle; 5) Sorting, ranking and grouping of the cross-sectioning profiles by gradient slope degree, to estimate differences in the geomorphic shapes.
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Sonuç & Katkı:Programming codes and snippets are presented for repeatability of the methods in similar research tasks.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Geomorphology · Slope Calculation · Gradient

Abstract

Multiple factors affect submarine geomorphology causing variations in the gradient slope: geological settings (rock composition, structure, permeability, erodibility of the materials), submarine erosion, gravity flows of water streams, tectonics, sediments from the volcanic arcs, transported by transverse submarine canyons. Understanding the slope geomorphology is important for the precise bathymetric mapping. However, analysis of such a complex geomorphic structure as ocean trench requires numerical computation and advanced statistical analysis of the data set. Such methods are proposed by R and Python programming languages that include libraries of machine learning algorithms for the data processing used in this research: {tidyverse}, {ggsignif}, {ggplot} and {magrittr} by R, StatsModels, Matplotlib, NumPy, Pandas and Seaborn by Python. The research workflow can be summarized in five steps: 1) Partial least squares regression analysis; 2) Violin plots, modified box plot approach; 3) Modelling variations of depth and slope gradient, facetted in multi-panel plots by 4 tectonic plates; 4) Calculating normalized steepness angle; 5) Sorting, ranking and grouping of the cross-sectioning profiles by gradient slope degree, to estimate differences in the geomorphic shapes. As a result of the ranking performed in step 5, slopes were classified into five classes based on the calculated tangent angles: strong, very strong, extreme, steep, very steep. The results show differences in the gradient slope between various segments of the Mariana Trench located in four tectonic plates: Mariana, Caroline, Pacific and Philippine Sea, performed by statistical data modelling. Programming codes and snippets are presented for repeatability of the methods in similar research tasks.

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Bu Makaleye Atıf Yap

LEMENKOVA, P. (2019). Calculating slope gradient variations in the submarine landforms by R and Python statistical libraries. Manas Journal of Engineering, Cilt: 7 - Sayı: 2, 99–113. https://ajindex.com/articles/281462

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Kaynakça (15)

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