RETRACTED: UFuzzy: Fuzzy Models with Universum

Tencer, Lukas; Režnáková, Marta; Cheriet, Mohamed

doi:10.1016/j.asoc.2016.05.041

Cited by 3 publications

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, uncertainty processing is necessary for factors with incomplete information and ambiguity. Zadeh et al, He and Zhang, Abbasi-Ghalehtaki et al, Tencer et al, Rajak et al, and Jafelice et al proposed the concept of fuzzy set in 1965, which provided a method to solve fuzzy problems [33][34][35][36][37][38]. Since then, many scholars have extensively studied the combination of fuzzy sets and GIS (geographic information system), which provides a quantitative description method for handling complex systems [39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Yang

Weiling

et al. 2018

Complexity

View full text Add to dashboard Cite

The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

show abstract