A Novel Spatial Belief Rule-Based Intelligent Decision Support System

Real-world decision problems are usually associated with a certain geographical area, and therefore can and should be geographically referenced in most of cases. While traditional Decision Support Systems (DSSs) ignore the spatial dimension of the problem, most Geographic Information System (GIS)-based Spatial Decision Support Systems (SDSSs) focus mainly on the spatial analysis of the problem, avoiding other relevant factors like uncertainty and incompleteness of data sets. This research is based on a recently developed intelligent belief rule-based DSS, called RIMER+, which is shown to be capable of capturing vagueness, incompleteness, uncertainty, and nonlinear causal relationships in an integrated way. The main contribution of this research is to explore the possibilities of achieving a higher degree of integration of DSSs in a GIS environment, i.e., integration of RIMER+ within GIS system by using an embedded approach, which not only enhances further the capability and applicability of the RIMER+ by integrating the spatial component of the problem into the decision making process, but also takes advantage of the GIS software capabilities in terms of spatial analysis and visualization. Finally, this research employs a comparative case study to demonstrate performance of the proposed Spatial RIMER+ methodology against the well-known Geographically Weighted Regression (GWR) methodology.