CLUSTERING OF RAINFALL DISTRIBUTION PATTERNS IN PENINSULAR MALAYSIA USING TIME SERIES CLUSTERING METHOD

Abstract

Time series clustering technique was used in this study to categorize the locations in Peninsular Malaysia according to the similarity of rainfall distribution patterns. Daily rainfall time series data from 12 meteorological observation stations across Peninsular Malaysia have been considered for this study. Four dissimilarity measure methods were examined and compared in terms of accuracy and suitability, namely Euclidean distance (ED), complexity-invariant distance (CID), correlation-based distance (COR) and integrated periodogram-based distance (IP). The average silhouette width (ASW) was used to determine the optimal group number for the rainfall time series data. Using Ward’s hierarchical clustering method, this study found that the rainfall time series in Peninsular Malaysia can be divided into four regions of homogeneous climate zones. Based on the results, the IP was the most suitable dissimilarity measures for clustering rainfall time series data in Peninsular Malaysia, except during the Southwest Monsoon where the COR performed better.