Abstract
Water is critical for long-term socioeconomic growth. Rainfall is one of the water sources.. Many factors contribute to disparities of rainfall patterns in Indonesia. The study aims to detect and map the implication of clustering annual rainfall patterns in each province in Indonesia using the annual provincial rainfall data 2011 to 2015. K Means Clustering using an unsupervised learning technique was applied for rainfall events by employing rainfall measurement data from one of Indonesia's rain stations in each province. The clustering results indicate that the two provinces in cluster I have very low water availability, cluster II has moderate rainfall with rainy days under 200 days, i.e. wet months of seven months, and cluster III has a very wide variation in maximum and minimal rainfall. The results demonstrate the trend of locations vulnerable to water availability in the provinces of Central and West Sulawesi. Rainfall should be managed as a potential water source in face of the extreme dry season, particularly in three provinces in cluster III: South Sumatra, Papua, and Maluku. Rainfall clustering in Indonesia is expected to be used as a dataframe, as well as a preliminary analysis to reinforce local rainfall, as well as basis for managing and exploiting rainfall, as well as planning mitigation and adaptation to climate change in Indonesia.
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