Fuzzy topological simulation for deducing in GIS
- PDF / 359,954 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 60 Downloads / 176 Views
ORIGINAL PAPER
Fuzzy topological simulation for deducing in GIS Rouzbeh Shad & Arefeh Shad & Mohammad Saadi Mesgari & Hossein Aghamohammadi & Damoon Molaei
Received: 6 April 2009 / Accepted: 11 November 2009 / Published online: 1 December 2009 # Società Italiana di Fotogrammetria e Topografia (SIFET) 2009
Abstract The proposed methodology relies on the fuzzy nine-intersection matrix which is a generalization of the crisp four-intersection matrix for topological similarity computing. The similarity computation between 3D fuzzy matrix and 3D crisp nine-intersection matrix enables the decision variables to be derived. Decision variables, which are used for deducing and drawing conclusion, are consisted of semantic parts and quantifiers (type and strength of relations). Since these variables are dependent on the boundary directly, it is essential to present an efficient method for defining 3D fuzzy boundary. So, in this paper, we complete the information about how we can define fuzzy boundaries between two 3D phenomena and present a new procedure for simulation of 3D spatial
R. Shad (*) Civil Department, Faculty of Engineering, Ferdosi University of Mashhad, Mashhad, Iran e-mail: [email protected] A. Shad Department of Industrial Engineering, Amirkabir University, Tehran, Iran e-mail: [email protected] M. S. Mesgari : H. Aghamohammadi : D. Molaei Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, No. 1346, Mirdamad Cross, Valiasr St., Tehran, Iran M. S. Mesgari e-mail: [email protected] H. Aghamohammadi e-mail: [email protected] D. Molaei e-mail: [email protected]
topology in a deductive geographic information system (GIS). Therefore, a fuzzy knowledge-base system and an inference engine will be shown results for deduction in GIS environment. Keywords GIS . Fuzzy . Topology . Deduction
Introduction In geographic information system (GIS) medium, usually uncertain spatial features are revealed schematically. In this manner, mathematical models and simulation techniques are used in processing, analyzing, and in decision making of uncertain data (Burrough 1996). This uncertainty maybe is originated from different resources such as nature of phenomena, human knowledge, and the limitations of the meanings (Shi and Lui 2007). Despite this fact, crisp solutions are widely used in GIS for modeling the natural phenomena. These approaches impose some limitations in different disciplines such as soil science (McBratney and Odeh 1997), engineering (Kosko 1997), object-oriented modeling (Cross 2001; Jonathan et al. 2001; Ma et al. 2001), and data mining (Clementini et al. 2000). Thus, the concepts of classical set theory and crisp boundary may not be suitable for handling the uncertainty inherent in such problems (Wang et al. 1990). The boundary, in simple words, is the outer limits of feature that is defined for better understanding and recognizing of objects. The kinds of feature boundaries depend on their material and their functional and temporal properties. Yet, o
Data Loading...