scholarly journals On the combination of rotated principal component analysis regionalization technique and linear regression in seasonal rainfall prediction

2021 ◽  
Author(s):  
Chibuike Chiedozie Ibebuchi
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Austral Summer ◽  
Principal Component ◽  
Summer Rainfall ◽  
Rainfall Anomaly ◽  
Relative Vorticity ◽  
Seasonal Rainfall ◽  
Sea Level Pressure ◽  
Rainfall Region

Abstract This study considers the selection of predictors for regional rainfall based on dynamical considerations; for this reason, a regionalization technique that can preserve the underlying physics of rainfall was used in obtaining landmasses and local oceanic domains that are spatially coherent. For the study region (Africa, south of the equator), the adjacent oceans play a vital role in the seasonal rainfall variability at the landmasses; thus uncovering the complex nature of the multivariate relationship between rainfall coherent landmasses and local oceanic domains will enhance the construction of oceanic indices as predictors of seasonal rainfall at specific landmasses using linear regression analysis. Among different cluster analysis techniques, the rotated principal component analysis (PCA) is both fuzzy and allows the overlapping of the classified data set, which makes it a better choice for geophysical research that aims to regionalize continuous data such as rainfall. 10 regions with spatially homogeneous austral summer monthly rainfall totals were classified using the rotated PCA; some classified regions featured landmasses that are spatially coherent with the adjacent ocean, which qualifies them to be further considered on how rainfall anomaly and other physical parameters related to rainfall (e.g. convergence, relative vorticity, and sea level pressure) at the adjacent oceans explain the variations in austral summer rainfall anomaly at the homogeneous landmasses. The analysis of the physical mechanisms associated with the time development of the selected rainfall regions reveals that at the west-central equatorial rainfall region, variations in relative vorticity and convergence are associated with the development of the rainfall region; whereas at the central domains of southern Africa, variations in the patterns of sea level pressure, relative vorticity and convergence at the landmasses, the tropical and the southwest Indian Ocean can be associated with the development of the distinct rainfall sub-regions. The predictability of austral summer rainfall anomaly at the homogeneous landmasses using appropriate predictors at the adjacent local oceanic domains was relatively more accurate at the deep tropics, possibly due to the dominating mechanism of convergence in controlling the tropical rainfall.

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

Dimensioni e direttrici del mutamento socio-economico in prospettiva territoriale dal 1951 a oggi

2013 ◽  
pp. 78-92
Author(s):  
Domenico Maddaloni ◽  
Fiorenzo Parziale
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Southern Italy ◽  
Principal Component ◽  
Research Strategy ◽  
Two Phase ◽  
Economic Changes ◽  
Local Contexts ◽  
Italian Provinces ◽  
History Of

In this study we go back to examine the economic and sociological changes throughout the local contexts and divisions of our country. The instrument used is a research strategy that combines a two-phase principal component analysis developed by Di Franco and Marradi with multiple linear regression. From data inherent to four key moments in the recent history of Southern Italy and the whole country - 1951, 1971, 1991 and 2007 - we obtain four «photographs» of dimensions that clarify the structure of the selected variables. We then propose two models of path analysis that underline the causal links between the factors emerged from the PCA, in order to reconstruct the socio-economic changes in the Italian provinces from 1951 to 2007.

The Quantitative Evaluation on the Response of Climate Change upon the Sustainability of the Agricultural Production

2010 ◽  
Vol 129-131 ◽  
pp. 1161-1165
Author(s):  
Lin Chun Hou ◽  
Hui Qin Li
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Agricultural Production ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Environmental Condition ◽  
Land Utilization ◽  
Agricultural Society ◽  
Per Capita

The aim: quantitatively evaluate the response of climate change upon the sustainability of the agricultural production. The method: the paper selected two regions (Hubei and shan’xi province) which represented different climate environment, utilized modern statistic data, Principal Component Analysis and multivariate linear regression to quantitatively evaluate the influence of climate change upon agricultural production through isolating climate environment from arable area, land utilization and management and landform and so on. The conclusion: The study indicated that when environmental condition turned good to agriculture, the function of environmental condition to agriculture relatively decreased; the capability of agricultural society and production decreased too, and people could select the land to cultivate, where agricultural productivity is higher. And that when environmental condition turned bad to agriculture, the function of environmental condition to agriculture relatively increased; the capability of agricultural society and production increased, too; people could not put emphasis on the land where agricultural productivity is higher, whereas focused on productivity per capita.

scholarly journals Long-Lead Seasonal Prediction of China Summer Rainfall Using an EOF–PLS Regression-Based Methodology*,+

2016 ◽  
Vol 29 (5) ◽  
pp. 1783-1796 ◽  
Author(s):  
Wen Xing ◽  
Bin Wang ◽  
So-Young Yim
Keyword(s):  
Air Temperature ◽  
Dynamic Models ◽  
Principal Component ◽  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Rainfall Anomaly ◽  
Pls Regression ◽  
Rainfall Prediction ◽  
Forecast Validation ◽  
Forecast Models

Abstract Considerable year-to-year variability of summer rainfall exposes China to threats of frequent droughts and floods. Objective prediction of the summer rainfall anomaly pattern turns out to be very challenging. As shown in the present study, the contemporary state-of-the-art dynamical models’ 1-month-lead prediction of China summer rainfall (CSR) anomalies has insignificant skills. Thus, there is an urgent need to explore other ways to improve CSR prediction. The present study proposes a combined empirical orthogonal function (EOF)–partial least squares (PLS) regression method to offer a potential long-lead objective prediction of spatial distribution of CSR anomalies. The essence of the methodology is to use PLS regression to predict the principal component (PC) of the first five leading EOF modes of CSR. The preceding December–January mean surface temperature field [ST; i.e., SST over ocean and 2-m air temperature (T2m) over land] is selected as the predictor field for all five PCs because SST and snow cover, which is reflected by 2-m air temperature, are the most important factors that affect CSR and because the correlation between each mode and ST during winter is higher than in spring. The 4-month-lead forecast models are established by using the data from 1979 to 2004. A 9-yr independent forward-rolling prediction is made for the latest 9 yr (2005–13) as a strict forecast validation. The pattern correlation coefficient skill (0.32) between the observed and the 4-month-lead predicted patterns during the independent forecast period of 2005–13 is significantly higher than the dynamic models’ 1-month-lead hindcast skill (0.04), which indicates that the EOF–PLS regression is a useful tool for improving the current seasonal rainfall prediction. Issues related to the EOF–PLS method are also discussed.

The Research of Wine Quality Evaluation Based on Multiple Linear Regression

2013 ◽  
Vol 756-759 ◽  
pp. 2489-2493
Author(s):  
Huai Hui Liu ◽  
Wen Long Ji ◽  
Peng Zhang ◽  
Chuan Wen Yao
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Wine Grape ◽  
Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

scholarly journals Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

2018 ◽  
Vol 26 (0) ◽  
pp. 170-176 ◽  
Author(s):  
Stephen J.H. Yang ◽  
Owen H.T. Lu ◽  
Anna Y.Q. Huang ◽  
Jeff C.H. Huang ◽  
Hiroaki Ogata ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Academic Performance ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component ◽  
Component Analysis

scholarly journals Relationship between the Physical Properties and Hand of Jean Fabric

2016 ◽  
Vol 16 (3) ◽  
pp. 138-145 ◽  
Author(s):  
Atsushi Kawamura ◽  
Chunhong Zhu ◽  
Julie Peiffer ◽  
KyoungOk Kim ◽  
Yi Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Physical Properties ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Evaluation System ◽  
Principal Component ◽  
Semantic Differential ◽  
Differential Method ◽  
Fabric Hand

Abstract We investigated the distinctive characteristics of jean fabrics (denim fabrics obtained from jeans) and compared the physical properties and the hand. We used 13 kinds of jean fabric from commercial jeans and 26 other fabric types. The physical properties were measured using the Kawabata evaluation system, and the fabric hand was evaluated by 20 subjects using a semantic differential method. To characterise the hand of jean fabrics compared with other fabrics, we used principal component analysis and obtained three principal components. We found that jean fabrics were characterised by the second principal component, which was affected by feelings of thickness and weight. We further characterised the jean fabrics according to ‘softness & smoothness’ and ‘non-fullness’, depending on country of origin and type of manufacturer. The three principal components were analysed using multiple linear regression to characterise the components according to the physical properties. We explained the hand of fabrics including jean fabrics using its association with physical properties.

scholarly journals Performance Analysis of Classifiers in Identifying NREM Sleep and Awaken Stages from EEG Signals

2020 ◽  
Vol 8 (6) ◽  
pp. 4321-4326
Keyword(s):  
Principal Component Analysis ◽  
Logistic Regression ◽  
Linear Regression ◽  
Expectation Maximization ◽  
Principal Component ◽  
Nrem Sleep ◽  
Component Analysis ◽  
Medical Procedure ◽  
Eeg Signals ◽  
Non Linear

Electroencephalogram is a medical procedure which helps in analyzing the activities of the brain through electrical signals. In this paper a simple classification technique of EEG signal into two stages as NREM sleep and awaken stages had been undertaken. Classifying these stages helps the physician to understand the patient's sleep disorder by knowing whether the person's brain is in NREM sleep or awaken stages. Physionet EEG signals are samples of 256 signals per second for 10 seconds duration is used in this work. Then the EEG samples properties are analyzed through various parameters like statistical features, entropy Pearson correlation coefficient, Power spectral density, scatter plots and Hilbert transform plots. The classification of NREM sleep and awaken stage is performed by the ten different classifiers broadly grouped into non linear and hybrid one. The classifiers used include Linear Regression, Non Linear Regression, Logistic Regression, Principal Component Analysis, Kernel Principal Component Analysis, Expectation Maximization, Compensatory Expectation Maximization, Expectation Maximization with Logistic Regression Compensatory Expectation Maximization with Logistic Regression, and Firefly. The performances of the classifiers are analyzed using regular parameters like sensitivity, accuracy, specificity, performance index. The highest accuracy of 95.575% is achieved with linear regression for awaken signal and an accuracy of 95.315% is achieved using kernel PCA for sleep signal.

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