Abstract:
Decision support systems, statistics and expert systems were some of the mainstay techniques used for modelling environmental phenomena. Now modelling systems utilise artificial intelligence (AI) techniques for the extra computational analysis they provide. Whilst operating in a toolbox environment and by adopting AI techniques, the geographic information system (GIS) modellers have greater options available for solving problems. This paper outlines a new approach in applying artificial intelligence techniques to solve spatial problems. The approach combines case-based reasoning (CBR) with geographic information systems and allows both techniques to be applied to solve spatial problems. More specifically this paper examines techniques applied to the problem of soil classification. Spatial cases are defined and analysed using the case-based reasoning techniques of retrieve, reuse, revise and retain. Once the structure of cases are defined a case base is compiled. When the case base is of sufficient size, the problem of soil classification is tested using this new approach. The problem is solved by searching the case base for another spatial phenomena similar to that which exists. Then the knowledge from that searched case is used to formulate an answer to the problem. A comparison of the results obtained by this approach and a traditional method of soil classification is then undertaken. This paper also documents the saving data concept in translating from decision trees to CBR. The logistics of the problems that are characteristic of case-based reasoning systems are discussed, for example, how should the spatial domain of an environmental phenomena be best represented in a case base? What are the constraints of CBR, what data are lost, and what functions are gained? Finally, the following question is posed: “to what real world level can the environment be modelled using GIS and case-based reasoning techniques”?
