scholarly journals Monthly and Seasonal Rainfall Forecasting in Southern Brazil Using Multiple Discriminant Analysis

2016 ◽  
Vol 31 (6) ◽  
pp. 1947-1960 ◽  
Author(s):  
Denilson Ribeiro Viana ◽  
Clóvis Angeli Sansigolo
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Skill Score ◽  
Seasonal Rainfall ◽  
Rainfall Time Series ◽  
Southern Brazil ◽  
Multiple Discriminant Analysis ◽  
Rainfall Forecasting ◽  
Global Precipitation Climatology Centre

Abstract A multiple discriminant analysis was employed to forecast monthly and seasonal rainfall in southern Brazil. The methodology used includes six steps: data acquisition, preprocessing, feature extraction, feature selection, classification, and evaluation. The predictors (atmospheric, surface, and oceanic variables) and predictand (rainfall) were obtained from the Twentieth Century Reanalysis (version 2), as well as from the HadISST1 (Met Office Hadley Centre) and Global Precipitation Climatology Centre (GPCC) databases. The definition of key regions (feature extraction step) was performed using spatial principal component analysis. In the selection step, the rainfall time series were allocated into terciles, which were related to the predictors via multiple discriminating analyses. The results revealed that ⅓ of the predictors are associated with atmospheric pressure and also emphasized the role of atmospheric circulation over the Antarctic region and its surroundings. Surface variables (albedo and soil moisture) were also of great importance in the forecasting. The average skill score (gain over climatology) was 29%. It is concluded that the proposed model is a reliable alternative for use in forecasting monthly and seasonal rainfall over southern Brazil.

scholarly journals Uncertainty in Estimated Trends Using Gridded Rainfall Data: A Case Study of Bangladesh

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 349 ◽  
Author(s):  
Mohamed Salem Nashwan ◽  
Shamsuddin Shahid ◽  
Xiaojun Wang
Keyword(s):  
Spatial Patterns ◽  
Similarity Index ◽  
Skill Score ◽  
Rainfall Data ◽  
Seasonal Rainfall ◽  
Annual Rainfall ◽  
Global Precipitation Climatology Centre ◽  
Rainfall Trends ◽  
Grid Points

This study assessed the uncertainty in the spatial pattern of rainfall trends in six widely used monthly gridded rainfall datasets for 1979–2010. Bangladesh is considered as the case study area where changes in rainfall are the highest concern due to global warming-induced climate change. The evaluation was based on the ability of the gridded data to estimate the spatial patterns of the magnitude and significance of annual and seasonal rainfall trends estimated using Mann–Kendall (MK) and modified MK (mMK) tests at 34 gauges. A set of statistical indices including Kling–Gupta efficiency, modified index of agreement (md), skill score (SS), and Jaccard similarity index (JSI) were used. The results showed a large variation in the spatial patterns of rainfall trends obtained using different gridded datasets. Global Precipitation Climatology Centre (GPCC) data was found to be the most suitable rainfall data for the assessment of annual and seasonal rainfall trends in Bangladesh which showed a JSI, md, and SS of 22%, 0.61, and 0.73, respectively, when compared with the observed annual trend. Assessment of long-term trend in rainfall (1901–2017) using mMK test revealed no change in annual rainfall and changes in seasonal rainfall only at a few grid points in Bangladesh over the last century.

Gear Fault Classification Using Kernel Discriminant Analysis

2006 ◽  
Vol 321-323 ◽  
pp. 1556-1559
Author(s):  
Wei Hua Li ◽  
Kang Ding ◽  
Tie Lin Shi ◽  
Guang Lan Liao
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Operating Conditions ◽  
Fault Classification ◽  
Kernel Principal Component Analysis ◽  
Kernel Discriminant Analysis ◽  
Vibration Signals ◽  
Gearbox Vibration ◽  
Gear Fault

This paper presents a study of KDA(kernel discriminant analysis) in gearbox failure feature extraction and classification. Experimental gearbox vibration signals measured from normal, gear small spall, gear severe spall and gear wear operating conditions are analyzed using either KPCA(kernel principal component analysis) or KDA as the feature extraction and fault classification methods. Experiment results indicate the effectiveness and thesuperiority of KDA for gear fault classification over KPCA.

scholarly journals Predicting biological parameters of estuarine benthic communities using models based on environmental data

2004 ◽  
Vol 47 (4) ◽  
pp. 613-627 ◽  
Author(s):  
José Souto Rosa-Filho ◽  
Carlos Emílio Bemvenuti ◽  
Michael Elliott
Keyword(s):  
Discriminant Analysis ◽  
Linear Regression ◽  
Predictive Models ◽  
Benthic Communities ◽  
Environmental Data ◽  
Biological Parameters ◽  
Southern Brazil ◽  
Multiple Discriminant Analysis ◽  
Sediment Characteristics ◽  
Benthic Assemblages

This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness) of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth). Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA), and the second based on Multiple Linear Regression (MLR). Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.

scholarly journals Aromatic Herbs Classification by using Discriminant Analysis Techniques

2017 ◽  
Vol 5 (3) ◽  
pp. 530
Author(s):  
N. F.M. Radzi ◽  
A. Che Soh ◽  
A. J. Ishak ◽  
M. K. Hasan ◽  
U. K. Mohamad Yusof
Keyword(s):  
Discriminant Analysis ◽  
Electronic Nose ◽  
Principal Component ◽  
Processing Technique ◽  
Family Group ◽  
Multiple Discriminant Analysis ◽  
Signal Processing Technique ◽  
Pca Method ◽  
Herb Species ◽  
Group Species

<p>An electronic nose was used to distinguish between selected herb samples according to their family group species. This paper aims to evaluate the potential of using the electronic nose to characterize three groups of families of twelve herb species based on the discriminant analysis approach. The feature extraction involves the use of a signal processing technique that simplifies classification and yields optimal results. Two discriminant techniques:- the principal component analysis (PCA) and the multiple discriminant analysis (MDA) were used to investigate the potential to distinguish herb species between several herbs within the same family group. The results showed that the twelve herb species can be better classified using the MDA method compared to the PCA method.</p>

scholarly journals A Report on Multilinear PCA Plus GTDA to Deal With Face Image

2016 ◽  
Vol 16 (1) ◽  
pp. 146-157 ◽  
Author(s):  
Fan Zhang ◽  
Xiaoping Wang ◽  
Ke Sun
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Extraction Methods ◽  
Subspace Analysis ◽  
Comparative Effect ◽  
The Past ◽  
Face Images ◽  
Facial Images ◽  
Multilinear Principal Component Analysis

Abstract Because face images are naturally two-dimensional data, there have been several 2D feature extraction methods to deal with facial images while there are few 2D effective classifiers. Meanwhile, there is an increasing interest in the multilinear subspace analysis and many methods have been proposed to operate directly on these tensorial data during the past several years. One of these popular unsupervised multilinear algorithms is Multilinear Principal Component Analysis (MPCA) while another of the supervised multilinear algorithm is Multilinear Discriminant Analysis (MDA). Then a MPCA+MDA method has been introduced to deal with the tensorial signal. However, due to the no convergence of MDA, it is difficult for MPCA+MDA to obtain a precise result. Hence, to overcome this limitation, a new MPCA plus General Tensor Discriminant Analysis (GTDA) solution with well convergence is presented for tensorial face images feature extraction in this paper. Several experiments are carried out to evaluate the performance of MPCA+GTDA on different databases and the results show that this method has the potential to achieve comparative effect as MPCA+MDA.

Sign in / Sign up

Export Citation Format

Share Document