The gravity recovery and climate experiment (GRACE) and GRACE-Follow on (FO) data provide valuable information about dynamic total water storage (TWS). The complexity of the computational process and the influence of various parameters on TWS changes are complicated in their interpretation. Principal component analysis (PCA) has been used to identify key components to amplify signals and reduce noise in observations. For this purpose, in this research, the Self-organizing map algorithm (SOM) has been used to cluster TWS in 4 categories. The results show that the western regions of Greenland and part of Antarctica are in the critical cluster and have a TWS rate of about –0.2 m/year, which indicates the melting of ice in these regions. The advantage of PCA-SOM is the easy interpretation of TWS, which reduces the impact of seasonal parameters, observation noise and measurement error, and facilitates global policy decisions in the face of climate change.
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