A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

Nor, Siti Mariana Che Mat; Shaharudin, Shazlyn Milleana; Ismail, Sarimah; Kismiantini, Kismiantini

doi:10.13189/eer.2021.090303

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…CA is an unsupervised multivariate analysis which classifies the given data into similar overlapping or non-overlapping groups and helps to group variables into clusters according to the high similarity of their features, such as geographical, physical, statistical or stochastic properties [33,34]. The CA can be separated into two types: hierarchical and non-hierarchical [18].…”

Section: Hca Of Rainfall Seriesmentioning

confidence: 99%

Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

Basri

Shaharudin

Kismiantini

et al. 2021

IJGI

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Monthly precipitation data during the period of 1970 to 2019 obtained from the Meteorological, Climatological and Geophysical Agency database were used to analyze regionalized precipitation regimes in Yogyakarta, Indonesia. There were missing values in 52.6% of the data, which were handled by a hybrid random forest approach and bootstrap method (RF-Bs). The present approach addresses large missing values and also reduces the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) in the search for the optimum minimal value. Cluster analysis was used to classify stations or grid points into different rainfall regimes. Hierarchical clustering analysis (HCA) of rainfall data reveal the pattern of behavior of the rainfall regime in a specific region by identifying homogeneous clusters. According to the HCA, four distinct and homogenous regions were recognized. Then, the principal component analysis (PCA) technique was used to homogenize the rainfall series and optimally reduce the long-term rainfall records into a few variables. Moreover, PCA was applied to monthly rainfall data in order to validate the results of the HCA analysis. On the basis of the 75% of cumulative variation, 14 factors for the Dry season and the Rainy season, and 12 factors for the Inter-monsoon season, were extracted among the components using varimax rotation. Consideration of different groupings into these approaches opens up new advanced early warning systems in developing recommendations on how to differentiate climate change adaptation- and mitigation-related policies in order to minimize the largest economic damage and taking necessary precautions when multiple hazard events occur.

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Section: Hca Of Rainfall Seriesmentioning

confidence: 99%

Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

Basri

Shaharudin

Kismiantini

et al. 2021

IJGI

Self Cite

View full text Add to dashboard Cite

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Statistical Modeling of RPCA-FCM in Spatiotemporal Rainfall Patterns Recognition

et al. 2022

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This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast.

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A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

Cited by 2 publications

References 16 publications

Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

Regionalization of Rainfall Regimes Using Hybrid RF-Bs Couple with Multivariate Approaches

Statistical Modeling of RPCA-FCM in Spatiotemporal Rainfall Patterns Recognition

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