Site selection is a type of GIS analysis that is used to determine the best site for something and fuzzy logic is one site selection method. It assigns membership values to locations that range from 0 to 1 and is commonly used to find ideal habitat for plants and animals. This article examines fuzzy logic and explains how and when to use it. Assessment of soil fertility seems to be a crucial step in appropriate site-specific management for crop production especially for sugar beet cultivation in the Northeastern region of Iran. For preparing the soil fertility zonation map for sugar beet production, the most important soil chemical parameters and nutrient elements affecting soil fertility in 0–30 cm soil depth were analyzed and. Both modules integrate fuzzy logic concepts and functions under a GIS environment, meaning that they are designed to function using digital maps and relational geodatabases. Moreover, the system can support decision making regardless of certain scales and . This paper discusses and compares the potential application of the evidential belief function model and fuzzy logic inference system technique for spatial delineation of a groundwater artesian zone boundary in an arid region of central Iraq. using EBF and FL prediction models under a GIS platform. spatial pattern analysis was first.

Fuzzy Inference Process. Fuzzy inference is a process of mapping from an input to an output using fuzzy logic as discussed above. The process of fuzzy inference involves assigning Membership Functions (in Boolean function will be a square wave 0 or 1), applying logical operations, and If-Then, and or-Rules. Erosion Modeling in a Raster-Based GIS with Fuzzy Logic Hans W. Guesgen Computer Science Department University of Auckland Auckland, New Zealand between environmental factors such as soil, topography, drainage, rainfall, and land use patterns. Due to the Fuzzy inference uses fuzzy rules to deduct new infor-mation from given information. Urban growth occurs in conjunction with a series of decision-making processes and is, on the whole, not deterministic but rather is the outcome of competing local demands and uncontrolled, chaotic processes. Fuzzy sets theory is ideally suited to treat the complexity and uncertainties in the decision-making process. This chapter presented an example of how fuzzy sets can be applied to model. Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia. Environmental Earth Sciences. v63 i2. Google Scholar; Pradhan, a. Application of an advanced fuzzy logic model for landslide susceptibility analysis.

Gale, Pipkin, and Leung were pioneers in introducing the fuzzy logic in the geographical domain. Much work has been published on the use of fuzzy logic in a spatial domain especially in land suitability modeling [18–23]. Most of them model a membership function through (1) a deterministic formula, (2) the use of a regression equation, or (3. Zhu,A.X., B. Hudson, J. E. Burt, and K. Lubich, “Soil mapping using GIS, expert knowledge and fuzzy logic,” Soil Science Society of America Journal, Vol. 65, pp. Fuzzy soil inference software was used to map soils in project areas in Montana and Wisconsin. The fuzzy membership/similarity representation of data is described. Using a fuzzy logic decision system to optimize the land suitability evaluation for a sprinkler irrigation method MRM () Physical land suitability evaluation for specific cereal crops using GIS at Mashhad Plain Dane, JH, Topp, GC (eds), Methods of Soil Analysis. Part 4 Physical Methods. Soil Science Society of America Book Series 5. Under fuzzy logic, the soil at a given pixel (unit area) can be assigned to more than one soil class with varying degrees of class assignment (Burrough et al., ). These degrees of class assignment are referred to as fuzzy memberships. Fuzzy logic (Zadeh, ) has been effectively applied as an alternative to Boolean logic, weighted linear.