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

References

• Adesah, R.S., et al., 2017. Type-2 fuzzy curve model. Journal of Physics. Conference series, 890, 12088.
   Google Scholar
• Burrough, P. and Frank, A., 1996. On the relation between spatial concepts and geographic objects. In: Geographic objects with indeterminate boundaries. London: Taylor & Francis.
   Google Scholar
• Burrough, P.A., 1989. Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science, 40 (3), 477–492.
   Google Scholar
• Castro, J.R., et al., 2007. Hybrid learning algorithm for interval type-2 fuzzy neural networks. In: 2007 IEEE International Conference on Granular Computing (GRC 2007). IEEE, 157.
   Google Scholar
• Chen, J., 2009. Fuzzy theory based spatiotemporal data model and algorithm solution. Geomatics & Spatial Information Technology, 32, 114–116.
   Google Scholar
• Cross, V. and Firat, A., 2000. Fuzzy objects for geographical information systems. Fuzzy Sets Systems, 113, 19–36.
   Web of Science ®Google Scholar
• Dilo, A., 2006. Representation of and reasoning with vagueness in spatial information: a system for handling vague objects. Enschede: University of Twente.
   Google Scholar
• Fisher, P., 2000. Sorites paradox and vague geographies. Fuzzy Sets and Systems, 113 (1), 7–18.
   Web of Science ®Google Scholar
• Fisher, P., Cheng, T., and Wood, J., 2007. Higher order vagueness in geographical information: empirical geographical population of type N fuzzy sets. Geoinformatica, 11 (3), 311–330.
   Web of Science ®Google Scholar
• Griffith, D.A., Wong, D.W., and Chun, Y., 2015. Uncertainty-related research issues in spatial analysis. Uncertainty Modelling and Quality Control for Spatial Data, 1–11.doi:https://doi.org/10.1201/b19160-3.
   Google Scholar
• Guo, J. and Cui, W., 2012. Modeling and metrics of higher order fuzzy geographical phenomena. Acta Geodaetica et Cartographica Sinica, 41, 139–146.
   Google Scholar
• Guo, J., 2014. Geometric properties of interval type-II fuzzy regions. Journal of Intelligent and Fuzzy Systems, 26, 563–575.
   Web of Science ®Google Scholar
• Guo, J., Shao, X., and Mo, X., 2018. Qualitative topological relations between interval type‐2 fuzzy spatial objects. Transactions in GIS, 22 (6), 15961631.
   Web of Science ®Google Scholar
• Guo, J., Wang, Z., and Duan, Y., 2019. A fuzzy semantic spatial partitioning model of regions and applications in understanding remote sensing data. Journal of Intelligent & Fuzzy Systems, 36 (1), 689–707.
   Web of Science ®Google Scholar
• Guo, J.F. and Cui, T.J., 2012. Discussion on type-I fuzzy boundary and research on boundary definition of high order fuzzy region. TELKOMNIKA Indonesian Journal of Electrical Engineering, 10 (6), 1207–1213.
   Google Scholar
• Hwang, C. and Rhee, F.C.H., 2007. Uncertain fuzzy clustering: interval type-2 fuzzy approach to C-means. IEEE Transactions on Fuzzy Systems, 15 (1), 107–120.
   Web of Science ®Google Scholar
• Jifa, G. and Tiejun, C., 2014a. Geometric properties of interval type-II fuzzy regions. Journal of Intelligent & Fuzzy Systems, 26 (2), 563–575.
   Web of Science ®Google Scholar
• Jifa, G. and Tiejun, C., 2014b. Topological relation analysis between high-order fuzzy regions based on fuzzy logic. Journal of Intelligent & Fuzzy Systems, 26, 2057–2071.
   Web of Science ®Google Scholar
• John, R. and Coupland, S., 2007. Type‐2 fuzzy logic: a historical view. IEEE Computational Intelligence Magazine, 2 (1), 57–62.
   Web of Science ®Google Scholar
• Liang, Q. and Mendel J.M., 2000. Interval type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy Systems, 8 (5), 535–550.
   Web of Science ®Google Scholar
• Liu, Y., Yuan, Y., and Gao, S., 2019. Modeling the vagueness of areal geographic objects: a categorization system. ISPRS International Journal of Geo-Information, 8, 306.
   Web of Science ®Google Scholar
• Pfoser, D. and Tryfona, N., 2001. Capturing fuzziness and uncertainty of spatiotemporal objects. Advances in Databases and Information Systems, 2151, 112–126.
   Google Scholar
• Schneider, M., 1999. Uncertainty management for spatial data in databases: fuzzy spatial data types. LNCS, 1651, 330–351.
   Google Scholar
• Smith, B. and Varzi, A.C., 2000. Fiat and bona fide boundaries. Philosophy and Phenomenological Research, 401–420.
   Web of Science ®Google Scholar
• Sözer, A., et al., 2008. Modeling and querying fuzzy spatiotemporal databases. Information Sciences, 178 (19), 3665–3682.
   Web of Science ®Google Scholar
• Tang, X., 2004. Spatial object modeling in fuzzy topological spaces with applications to land cover change. Enschede: University of Twente.
   Google Scholar
• Wan, S., Lan, Z., and Liu Y., 2017. The response of wetland vegetation cover to hydrological regime change in Poyang Lake during the wet season based on NDVI index. Journal of Nanchang University (Natural Science), 41 (4), 341–348.
   Google Scholar
• Yang, X., Lam, H., and Wu, L., 2019. Membership-dependent stability conditions for type-1 and interval type-2 T–S fuzzy systems. Fuzzy Sets and Systems, 356, 44–62.
   Web of Science ®Google Scholar
• Yin, S., et al., 2013. Temporal and spatial change of vegetation cover in Jiangxi Province based on NDVI. Journal of Central China Normal University (Natural Science Edition), 47, 129–135.
   Google Scholar
• Zadeh, L., 1965. Fuzzy sets. Information and Control, 8, 338–353.
   Web of Science ®Google Scholar
• Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8 (3), 199–249.
   Web of Science ®Google Scholar
• Zhan, F.B., 1998. Approximate analysis of binary topological relations between geographic regions with indeterminate boundaries. Soft Computing, 2 (2), 28–34.
   Google Scholar
• Zhu, Z. and Du, G., 2006. Modelling spatial vagueness based on type-2 fuzzy set. Journal of Zhejiang University Science A 7, 250–256.
   Google Scholar