Regional Taiwan rainfall frequency analysis using principal component analysis, self-organizing maps and L-moments

2012 ◽  
Vol 43 (3) ◽  
pp. 275-285 ◽  
Author(s):  
Lu-Hsien Chen ◽  
Yu-Ting Hong
Keyword(s):  
Principal Component Analysis ◽  
Frequency Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Return Periods ◽  
Self Organizing Maps ◽  
Ungauged Sites ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Homogeneous Regions

The objective of this paper is to propose an approach, which consists of principal component analysis (PCA), self-organizing maps (SOM) and the L-moment method, for improving estimation of desired rainfall quantiles of ungauged sites. Firstly, PCA is applied to obtain the principal components. Then SOM is applied to group the rain gauges into specific clusters and the number of clusters can be objectively decided by visual inspection. Moreover, the L-moment based discordancy and heterogeneity are used to test whether clusters may be acceptable as being homogeneous. After the gauges are grouped into specific clusters, the homogeneous regions are then delineated. Finally, goodness-of-fit measure is used to select the regional probability distributions and the design rainfall quantiles with various return periods for each region can be estimated. The proposed approach is applied to analyze and quantify regional rainfalls in Taiwan. The proposed approach is a robust and efficient way for regional rainfall frequency analysis. Moreover, one can easily assign an ungauged site to a previously defined cluster according to a map of homogeneous regions. Therefore, the proposed approach is expected to be useful for providing the design rainfall quantiles with various return periods at ungauged sites.

scholarly journals The Effect of Sample Size on Bivariate Rainfall Frequency Analysis of Extreme Precipitation

Proceedings ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 19 ◽  
Author(s):  
Nikoletta Stamatatou ◽  
Lampros Vasiliades ◽  
Athanasios Loukas
Keyword(s):  
Sample Size ◽  
Frequency Analysis ◽  
Extreme Precipitation ◽  
Goodness Of Fit ◽  
Return Periods ◽  
Goodness Of Fit Test ◽  
Maximum Rainfall ◽  
Rainfall Frequency ◽  
Rainfall Frequency Analysis ◽  
Pseudo Likelihood Estimation

The objective of this study is to compare univariate and joint bivariate return periods of extreme precipitation that all rely on different probability concepts in selected meteorological stations in Cyprus. Pairs of maximum rainfall depths with corresponding durations are estimated and compared using annual maximum series (AMS) for the complete period of the analysis and 30-year subsets for selected data periods. Marginal distributions of extreme precipitation are examined and used for the estimation of typical design periods. The dependence between extreme rainfall and duration is then assessed by an exploratory data analysis using K-plots and Chi-plots and the consistency of their relationship is quantified by Kendall’s correlation coefficient. Copulas from Archimedean, Elliptical, and Extreme Value families are fitted using a pseudo-likelihood estimation method, evaluated according to the corrected Akaike Information Criterion and verified using both graphical approaches and a goodness-of-fit test based on the Cramér-von Mises statistic. The selected copula functions and the corresponding conditional and joint return periods are calculated and the results are compared with the marginal univariate estimations of each variable. Results highlight the effect of sample size on univariate and bivariate rainfall frequency analysis for hydraulic engineering design practices.

scholarly journals SLIC Superpixel-Based l2,1-Norm Robust Principal Component Analysis for Hyperspectral Image Classification

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 479 ◽  
Author(s):  
Baokai Zu ◽  
Kewen Xia ◽  
Tiejun Li ◽  
Ziping He ◽  
Yafang Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Curse Of Dimensionality ◽  
Hyperspectral Images ◽  
Robust Principal Component Analysis ◽  
Superpixel Segmentation ◽  
Homogeneous Regions

Hyperspectral Images (HSIs) contain enriched information due to the presence of various bands, which have gained attention for the past few decades. However, explosive growth in HSIs’ scale and dimensions causes “Curse of dimensionality” and “Hughes phenomenon”. Dimensionality reduction has become an important means to overcome the “Curse of dimensionality”. In hyperspectral images, labeled samples are more difficult to collect because they require many labor and material resources. Semi-supervised dimensionality reduction is very important in mining high-dimensional data due to the lack of costly-labeled samples. The promotion of the supervised dimensionality reduction method to the semi-supervised method is mostly done by graph, which is a powerful tool for characterizing data relationships and manifold exploration. To take advantage of the spatial information of data, we put forward a novel graph construction method for semi-supervised learning, called SLIC Superpixel-based l 2 , 1 -norm Robust Principal Component Analysis (SURPCA2,1), which integrates superpixel segmentation method Simple Linear Iterative Clustering (SLIC) into Low-rank Decomposition. First, the SLIC algorithm is adopted to obtain the spatial homogeneous regions of HSI. Then, the l 2 , 1 -norm RPCA is exploited in each superpixel area, which captures the global information of homogeneous regions and preserves spectral subspace segmentation of HSIs very well. Therefore, we have explored the spatial and spectral information of hyperspectral image simultaneously by combining superpixel segmentation with RPCA. Finally, a semi-supervised dimensionality reduction framework based on SURPCA2,1 graph is used for feature extraction task. Extensive experiments on multiple HSIs showed that the proposed spectral-spatial SURPCA2,1 is always comparable to other compared graphs with few labeled samples.

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps, cluster, and Principal Component Analysis

2009 ◽  
Vol 43 (25) ◽  
pp. 3829-3836 ◽  
Author(s):  
Gabriel Ibarra-Berastegi ◽  
Jon Sáenz ◽  
Agustín Ezcurra ◽  
Unai Ganzedo ◽  
Javier Díaz de Argandoña ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Air Quality ◽  
Spatial Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Self Organizing Maps ◽  
Self Organizing

Régionalisation du régime des précipitations dans la région des Bois-francs et de l'Estrie par l'analyse en composantes principales

1998 ◽  
Vol 25 (6) ◽  
pp. 1050-1058 ◽  
Author(s):  
T O Siew-Yan-Yu ◽  
J Rousselle ◽  
G Jacques ◽  
V.-T.-V. Nguyen
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Regime ◽  
Regional Patterns ◽  
Air Masses ◽  
Orographic Effect ◽  
High Precipitation ◽  
Definition Of ◽  
Homogeneous Regions

A definition of homogeneous regions in terms of precipitation regime is achieved by the use of principal component analysis (PCA). The method has been shown to be a reliable regionalization tool even though it was applied to a territory showing rather complex physiography and high precipitation variation. Results based on the application of the PCA to the interstation correlation matrix of precipitation have indicated four distinct homogeneous regions. These regional patterns can be explained by the orographic effect and by the circulation of air masses within the study region.Key words: homogeneous regions, rainfall, principal component analysis, orographic effect.

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