Comprehensive assessment of microbial aggregation characteristics of activated sludge bioreactors using fuzzy clustering analysis

Wang, Gang; Wang, Yan; Liu, Li; Jin, Yan; Zhu, Ning; Li, Xing; Wang, Guoqing; Chen, Guowei

doi:10.1016/j.ecoenv.2018.06.096

Cited by 5 publications

(1 citation statement)

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“…The FCM algorithm is chosen because it introduces fuzzy partitioning and membership degree theory in clustering, which allows the unclassified area to simultaneously belong to different land use types [61]. At the same time, the FCM algorithm has a solid theoretical foundation and broad applications [62][63][64].…”

Section: Constructing the Membership Matrixmentioning

confidence: 99%

An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

He²,

Zhang

et al. 2019

IJGI

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Urban land use information is critical to urban planning, but the increasing complexity of urban systems makes the accurate classification of land use extremely challenging. Human activity features extracted from big data have been used for land use classification, and fusing different features can help improve the classification. In this paper, we propose a framework to integrate multiple human activity features for land use classification. Features were fused by constructing a membership matrix reflecting the fuzzy relationship between features and land use types using the fuzzy c-means (FCM) clustering method. The classification results were obtained by the fuzzy comprehensive evaluation (FCE) method, which regards the membership matrix as the fuzzy evaluation matrix. This framework was applied to a case study using taxi trajectory data from Nanjing, and the outflow, inflow, net flow and net flow ratio features were extracted. A series of experiments demonstrated that the proposed framework can effectively fuse different features and increase the accuracy of land use classification. The classification accuracy achieved 0.858 (Kappa = 0.810) when the four features were fused for land use classification.

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