Artificial intelligence and expert systems in geodata processing

Fisher, Peter; Mackaness, William; Peacegood, Gillian; Wilkinson, G. G.

doi:10.1177/030913338801200303

Cited by 12 publications

(2 citation statements)

References 31 publications

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“…The advantages of integrating a GIS with an expert system have been recognised by a number of authors (Burrough, 1986;Fedra, 1995;Fischer, 1994;Fisher et al, 1988;Lein, 1992;Leung, 1993;Robinson et al, 1987;Smith and Yiang, 1991;Zhu and Healey, 1992). These authors saw the expert system as having the potential to add intelligence to GIS tasks, e.g.…”

Section: Gis and Expert Systemsmentioning

confidence: 99%

GIS, expert systems, and interoperability

Lilburne¹,

Benwell²,

Buick³

1997

Transactions in GIS

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Integrating a GIS has been a common way to combine the functionality of two or more systems for some time. A three‐dimensional model of integration is described which shows the range of linkages that can be achieved. Extremely flexible and dynamic linkages between systems can now be created through the recent advances of client/server and object‐oriented technology. An expert system shell is coupled with a GIS to create a generic spatial rule‐based toolbox called SES (spatial expert shell). An expert system developer using this toolbox can transparently access spatial data and relationships from a GIS by linking application objects to spatial classes. These spatial classes include methods that format and send requests to the GIS server. Thus the linkage is determined at run‐time allowing a flexible interwoven interaction between the expert system and the GIS.

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Section: Gis and Expert Systemsmentioning

confidence: 99%

GIS, expert systems, and interoperability

Lilburne¹,

Benwell²,

Buick³

1997

Transactions in GIS

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“…As the demands often are restricted in reality, the emerging fuzzy set technology has aroused general attention in natural resources assessment and spatial data processing [1,2,4]. At the same time, the applications of artificial neural network technology in particular for expert systems also have been very popular within the domain [12,5,9,14]. However, problems still exist in two aspects.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy rule extraction from GIS data with a neural fuzzy system for decision making

Zheng¹,

Kainz²

1999

Proceedings of the 7th ACM International Symposium on Advances in Geographic Information Systems

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This study focuses on a new methodology to model a complex process of fuzzy rule acquisition from GIS data. The proposed method of neural fuzzy network described in this paper differs from the other methods in construction of the network by a simple representation of specific knowledge. Our goal is to use semantic If-Then rules instead of numerical values in the analytical models when uncertainty is involved in data. Such a method is expected to solve realistic problems in natural resource analysis and management. This paper discusses an adaptive neural network based on a fuzzy inference system that is able to learn fuzzy sets and fuzzy rules from data by the algorithm of steepest gradient descent. The fuzzy rules that are created by this approach can be very well interpreted to support decision-making. The method is tested in a case study of land suitability assessment. The result shows that the neural fuzzy network successfully extracted fuzzy rules with multidimensional spatial features obtained from GIS and simulated the decision process.

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Desert: A prototype expert system to advise on land degradation control

Bala

Fisher

1990

Land Degrad Dev

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Land degradation is a collection of processes causing the removal of land from agricultural productivity. Four major processes can be identified: soil erosion by water and wind, soil salinization, and vegetation degradation. Much knowledge exists on how these processes may be combated and ameliorated; however, it is in the minds of a relatively few, widely dispersed experts. The authors have developed a prototype computer-based expert system (DESERT) which provides advice on strategies for control of land degradation. The knowledge was acquired from experts by interviews. The knowledge was then formulated into two components. The first component enables identification of possible technical solutions, while the other imposes socio-economic and physical constraints on these technical solutions. Thus, a set of acceptable solutions is formulated. The results are presented here as a proof of concept in an attempt to stimulate further development. Later versions of the program will include problem diagnosis components, as well as a mechanism for prioritizing recommendations.

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Artificial intelligence and expert systems in geodata processing

Cited by 12 publications

References 31 publications

GIS, expert systems, and interoperability

GIS, expert systems, and interoperability

Fuzzy rule extraction from GIS data with a neural fuzzy system for decision making

Desert: A prototype expert system to advise on land degradation control

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