Calculating slope gradient variations in the submarine landforms by R and Python statistical libraries
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| Názov: | Calculating slope gradient variations in the submarine landforms by R and Python statistical libraries |
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| Autori: | Lemenkova, Polina |
| Prispievatelia: | Ocean University of China (OUC), China Scholarship Council (CSC), State Oceanic Administration (SOA), Marine Scholarship of China, Grant Nr. 2016SOA002, P.R.C. |
| Zdroj: | ISSN: 1694-7398 ; MANAS Journal of Engineering ; https://hal.archives-ouvertes.fr/hal-02425689 ; MANAS Journal of Engineering, 2019, 7 (2), pp.99-113. ⟨10.6084/m9.figshare.11454768⟩ ; https://dergipark.org.tr/tr/pub/mjen/issue/50947/560487. |
| Informácie o vydavateľovi: | HAL CCSD |
| Rok vydania: | 2019 |
| Zbierka: | Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
| Predmety: | Python, geomorphology, data analysis, statistics, Mariana trench, ACM: I.: Computing Methodologies, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.5: Model Development, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.1: Simulation Theory, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.7: Simulation Support Systems, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.1: Simulation Theory/I.6.1.0: Model classification, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING/I.6.1: Simulation Theory/I.6.1.2: Types of simulation (continuous and discrete), [INFO]Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, [SDE]Environmental Sciences, [SDU]Sciences of the Universe [physics], [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG] |
| Popis: | International audience ; This research focuses on the analysis of the submarine geomorphology in the Mariana Trench located in west Pacific Ocean. The research question is to identify variations in the geomorphic form and bathymetry in different segments of the trench. Technically, the paper applies Python and R programming statistical libraries for geospatial modelling of the data sets. The methodological approach of the statistical data analysis by scripting libraries aimed to visualize geomorphic variations in the 25 transect profiles of the trench. 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 changes in geomorphic variations is important for the correct geospatial analysis. However, modelling such a complex structure as hadal trench requires numerical computation and advanced statistical analysis of the data set. Such methods are proposed by R and Python programming languages. Current research presented usage of statistical libraries for the data processing: Matplotlib, NumPy, SciPy, Pandas, Seaborn, StatsModels by Python. The research workflow includes following steps: Partial least squares regression analysis; Ordinary Least Square (OLS); Violin plots and Bar plots for analysis of ranges of the bathymetric data; Isotonic Regression by StatsModels library; Data distribution analysis by Bokeh and Matplotlib libraries; Circular bar plots for sorting data by R; Euler-Venn diagrams for visualizing overlapping of attributes and factors by Python. As a result of the data analysis, the geomorphology of the trench slopes in 25 transecting profiles was modelled. The results achieved by the statistical data modelling show differences in the gradient slope in various segments of the trench depending on its spatial location. This ... |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | hal-02425689; https://hal.archives-ouvertes.fr/hal-02425689; https://hal.archives-ouvertes.fr/hal-02425689/document; https://hal.archives-ouvertes.fr/hal-02425689/file/Calculating%20slope%20gradient%20variations.pdf |
| DOI: | 10.6084/m9.figshare.11454768 |
| Dostupnosť: | https://hal.archives-ouvertes.fr/hal-02425689 https://hal.archives-ouvertes.fr/hal-02425689/document https://hal.archives-ouvertes.fr/hal-02425689/file/Calculating%20slope%20gradient%20variations.pdf https://doi.org/10.6084/m9.figshare.11454768 |
| Rights: | http://creativecommons.org/licenses/by-nc/ ; info:eu-repo/semantics/OpenAccess |
| Prístupové číslo: | edsbas.5E743F7 |
| Databáza: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | International audience ; This research focuses on the analysis of the submarine geomorphology in the Mariana Trench located in west Pacific Ocean. The research question is to identify variations in the geomorphic form and bathymetry in different segments of the trench. Technically, the paper applies Python and R programming statistical libraries for geospatial modelling of the data sets. The methodological approach of the statistical data analysis by scripting libraries aimed to visualize geomorphic variations in the 25 transect profiles of the trench. 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 changes in geomorphic variations is important for the correct geospatial analysis. However, modelling such a complex structure as hadal trench requires numerical computation and advanced statistical analysis of the data set. Such methods are proposed by R and Python programming languages. Current research presented usage of statistical libraries for the data processing: Matplotlib, NumPy, SciPy, Pandas, Seaborn, StatsModels by Python. The research workflow includes following steps: Partial least squares regression analysis; Ordinary Least Square (OLS); Violin plots and Bar plots for analysis of ranges of the bathymetric data; Isotonic Regression by StatsModels library; Data distribution analysis by Bokeh and Matplotlib libraries; Circular bar plots for sorting data by R; Euler-Venn diagrams for visualizing overlapping of attributes and factors by Python. As a result of the data analysis, the geomorphology of the trench slopes in 25 transecting profiles was modelled. The results achieved by the statistical data modelling show differences in the gradient slope in various segments of the trench depending on its spatial location. This ... |
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| DOI: | 10.6084/m9.figshare.11454768 |