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Clustering spatial-temporal precipitation data using wavelet transform and self-organizing map neural network
Kuo Chin Hsu
,
Sheng Tun Li
Department of Resources Engineering
Institute of Information Management
Research output
:
Contribution to journal
›
Article
›
peer-review
131
Citations (Scopus)
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Dive into the research topics of 'Clustering spatial-temporal precipitation data using wavelet transform and self-organizing map neural network'. Together they form a unique fingerprint.
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Keyphrases
Self-organizing Map Neural Network
100%
Hydrologic Regions
14%
Climate Crisis
14%
First Principals
14%
Silhouette Coefficient
14%
Homogeneous Cluster
14%
Data Analysis Methods
14%
Morlet Wavelet Function
14%
Spatially Homogeneous
14%
Dynamic Features
14%
Adaptation to Climate Change
14%
Water-reducing
14%
Precipitation Characteristics
14%
Precipitation Variation
14%
Scalogram Analysis
14%
Earth and Planetary Sciences
Self Organizing Map
100%
Geographic Location
33%
Water Environment
16%
Computer Science
Identify Cluster
16%
Silhouette Coefficient
16%
Homogeneous Cluster
16%
Climatic Change
16%
Wavelet Function
16%
Preprocessing Stage
16%
Total Variation
16%
Chemical Engineering
Neural Network
100%
Engineering
Spatiotemporal Characteristic
16%