AI Article Synopsis
- The system dynamics technique is effective for modeling and simulating spatial data infrastructures (SDIs), which focus on managing and sharing spatial data collaboratively.
- To enhance the incorporation of linguistic variables in SDI models, fuzzy logic is recommended, acknowledging that not all fuzzy models are suitable for this purpose.
- The study tests various fuzzy models and finds that the Average-Average inference combined with the Center of Area defuzzification method provides the best modeling of the dynamic behavior of SDI development.
Article Abstract
The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4777970 | PMC |
| http://dx.doi.org/10.1186/s40064-016-1922-1 | DOI Listing |
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