Abstract
Similarity analysis of small- and medium-sized watersheds mainly depends on manual work, and there is no complete automated analysis method. In order to solve this problem, we propose a similarity analysis method based on clustering ensemble model. First, the iterative clustering ensemble construction algorithm with weighted random sampling (WRS-CCE) is proposed to get great clustering collectives. Then, we combine spectral clustering with the fuzzy C-means method to design a consensus function for small- and medium-sized watershed data sets. Finally, the similarity analysis of small- and medium-sized watersheds is carried out according to the clustering results. Experiments show that the proposed clustering ensemble model can effectively find more potential similar watersheds and can output the similarity of these watersheds.
Highlights
Flood disaster is one of the most dangerous natural disasters
The spectral clustering algorithm is based on spectral partitioning, which converts clustering into multiple partitioning of undirected graphs [18]
Based on the characteristics small- and medium-sized and the theory of clustering ensemble, this paper studies the clustering ensemble model on the similarity analysis of small- and
Summary
Flood disaster is one of the most dangerous natural disasters. In recent years, the management of large rivers has tended to be perfect. Many small- and medium-sized watersheds lack corresponding hydrological data, they cannot carry out hydrological analysis, which leads to great difficulties in the management of these watersheds [1]. Due to the data of smalland medium-sized watersheds often having more characteristic dimensions, the existing watershed similarity analysis methods may not achieve ideal results for such high-dimensional and complex data sets. This paper puts forward a similarity analysis method of small- and medium-sized watershed based on clustering ensemble. This paper proposes a novel cluster ensemble model to analyze the similarity problem of smalland medium-sized watersheds. For small- and medium-sized watershed data, we propose an iterative clustering collective construction algorithm based on weighted random sampling (WRS-CCE) to construct clustering groups with high clustering quality and difference. The structure of this paper is as follows: Section 2 introduces the basic theories; Section 3 describes the model scheme based on clustering ensemble; Section 4 carries out experimental analysis; Section 5 summarizes the conclusions
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