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Clustering Spatial–temporal Precipitation Data Using Wavelet Transform and Self-organizing Map Neural Network by Kuo-Chin Hsu; Sheng-Tun Li is a Environmental Science article available to read on EtoBox.

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A data analysis method is proposed to cluster and explore spatio-temporal characteristics of the 22 years of precipitation data for Taiwan. The wavelet transform self-organizing map (WTSOM) framework combines the wavelet transform (WT) and a self-organizing map (SOM) neural network. WT is used to extract dynamic and multiscale features of the non-stationary precipitation time-series, and SOM is applied to objectively identify spatially homogeneous clusters on the high-dimensional wavelet-transformed feature space. Haar and Morlet wavelets are applied in the data preprocessing stage to preserve the desired characteristics of the precipitation data. A two-level SOM neural network is applied to identify clusters in the wavelet space in the clustering stage. The performance of clustering is evaluated using silhouette coefficients. The results indicate that singularities or sharp transitions are more significant than changes in the periodicity or data structure in the spatial-temporal precipitation data. The WTSOM results show that six clusters are optimal for both Haar and Morlet wavelet functions, but their corresponding geographic locations are different. The geographic locations of

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Author

Kuo-Chin Hsu; Sheng-Tun Li

Publisher

Elsevier Science; Elsevier ; Elsevier Ltd.; Elsevier BV (ISSN 0309-1708)

Published

2010

Language

EN

Field

Environmental Science (Physical Sciences)