Water level data from telemetry stations typically demonstrate diverse behaviors over time. Specific characteristics can be observed among distinct station groups that are different from others. Clustering time series data into a specified number of groups based on their similarity is an initial step for further analysis in water management analytics. Our main goal in this work is to develop a clustering framework based on a combination of feature representations, feature reduction techniques, as well as clustering algorithms. Thorough experiments on multiple combinations of these methods were conducted and compared. Based on collected water level data in Thailand, UMAP reduced representations of engineered features using HAC clustering with euclidean distance outperformed other methods. Its performance reached 0.8 Fowlkes-Mallows score. Out of 81 stations, only nine unclear cases were incorrectly clustered. Distinct behaviors with abrupt and frequent fluctuations could be perfectly identified.

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