scholarly journals An Application of Principal Component Analysis on Multivariate Time-Stationary Spatio-Temporal Data

2014 ◽  
Author(s):  
Stephan Stahlschmidt ◽  
Wolfgang K. HHrdle ◽  
Helmut Thome
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Temporal Data ◽  
Spatio Temporal

Modeling Spatio-Temporal Characteristics of Metocean Conditions During Hurricanes Using Deep Neural Networks

2020 ◽  
Author(s):  
Chi Qiao ◽  
Andrew T. Myers
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Dimension Reduction ◽  
Deep Neural Networks ◽  
Principal Component ◽  
Component Analysis ◽  
Offshore Structures ◽  
Complex Nature ◽  
Temporal Characteristics ◽  
Spatio Temporal

Abstract Surrogate modeling of the variability of metocean conditions in space and in time during hurricanes is a crucial task for risk analysis on offshore structures such as offshore wind turbines, which are deployed over a large area. This task is challenging because of the complex nature of the meteorology-metocean interaction in addition to the time-dependence and high-dimensionality of the output. In this paper, spatio-temporal characteristics of surrogate models, such as Deep Neural Networks, are analyzed based on an offshore multi-hazard database created by the authors. The focus of this paper is two-fold: first, the effectiveness of dimension reduction techniques for representing high-dimensional output distributed in space is investigated and, second, an overall approach to estimate spatio-temporal characteristics of hurricane hazards using Deep Neural Networks is presented. The popular dimension reduction technique, Principal Component Analysis, is shown to perform similarly compared to a simpler dimension reduction approach and to not perform as well as a surrogate model implemented without dimension reduction. Discussions are provided to explain why the performance of Principal Component Analysis is only mediocre in this implementation and why dimension reduction might not be necessary.

DYNAMICS AND SPATIO-TEMPORAL VARIABILITY OF ENVIRONMENTAL FACTORS IN EASTERN AUSTRALIA USING FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS

2010 ◽  
Vol 18 (04) ◽  
pp. 763-785 ◽  
Author(s):  
JUDIT K. SZABO ◽  
EUGENIO M. FEDRIANI ◽  
M. MANUELA SEGOVIA-GONZÁLEZ ◽  
LEE B. ASTHEIMER ◽  
MIKE J. HOOPER
Keyword(s):  
Principal Component Analysis ◽  
Temporal Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Functional Principal Component Analysis ◽  
Geographical Information ◽  
Spatial And Temporal Variability ◽  
Functional Principal Component ◽  
Eastern Australia ◽  
Spatio Temporal

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution. The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 1998–2004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types.

scholarly journals Spatio-temporal patterns of seasonal rainfall in Spain (1912-2000) using cluster and principal component analysis: comparison

2004 ◽  
Vol 22 (5) ◽  
pp. 1435-1448 ◽  
Author(s):  
D. Muñoz-Díaz ◽  
F. S. Rodrigo
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Temporal Patterns ◽  
Component Analysis ◽  
Atmospheric Dynamics ◽  
Seasonal Rainfall ◽  
Rainfall Distribution ◽  
Meteorology And Atmospheric Dynamics ◽  
Spatio Temporal

Abstract. In this work, cluster and principal component analysis are used to divide Spain in a limited number of climatically homogeneous zones, based on seasonal rainfall for 32 Spanish localities for the period 1912–2000. Using the hierarchical technique of clustering Ward's method, three clusters have been obtained in winter and spring, and four clusters have been obtained in summer and autumn. Results are similar to those obtained by applying principal component analysis. Centroid series of each cluster and principal component series of each EOF have been compared to analyze the temporal patterns. The comparison of both methods indicates that cluster analysis is suitable to establish spatio-temporal patterns of seasonal rainfall distribution in Spain. Key words. Meteorology and atmospheric dynamics (climatology; precipitation; general or miscellaneous)

Principal component analysis applied to a time series of MODIS images: the spatio-temporal variability of the Pantanal wetland, Brazil

2015 ◽  
Vol 23 (4) ◽  
pp. 737-748 ◽  
Author(s):  
Teodoro Isnard Ribeiro de Almeida ◽  
Natasha Costa Penatti ◽  
Laerte Guimarães Ferreira ◽  
Arielle Elias Arantes ◽  
Cibele Hummel do Amaral
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Temporal Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Spatio Temporal ◽  
Pantanal Wetland
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