Application of unsupervised clustering model based on graph embedding in water environment
Meng Fang,
Li Lyu,
Ning Wang
et al.
Abstract:Surface water monitoring data has spatiotemporal characteristics, and water quality will change with time and space in different seasons and climates. Data of this nature brings challenges to clustering, especially in terms of obtaining the temporal and spatial characteristics of the data. Therefore, this paper proposes an improved TADW algorithm and names it RTADW to obtain the spatiotemporal characteristics of surface water monitoring points. We improve the feature matrix in TADW and input the original time … Show more
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