Modeling vague spatiotemporal objects based on interval type-2 fuzzy sets

Fuzziness is an inherent property of geographical phenomena and the processes data acquisition, processing, analysis often introduce uncertainty. Existing methods predominantly use fuzzy set (FS) theory to capture fuzziness as spatial objects. However, this approach has a conceptual confusion regarding fuzziness, uncertainty, vagueness, membership degree expressed using accurate values that ignore Furthermore, FS-based lack vague temporal descriptor. Herein, vague-spatiotemporal-object model based on interval type-2 FS proposed express vagueness spatiotemporal To verify feasibility superiority method, clustering algorithm was used classify vegetation cover types Poyang Lake Plain, China. classification accuracy validated via field investigations, its ability identify wet season area verified annual water changes Lake. The results indicate that, compared with object FSs, method increases measure error vagueness.