Uncertain spatial reasoning of environmental risks in GIS using genetic learning algorithms.

Modeling the impact of air pollution is one of the most important approaches for managing damages to the ecosystem. This problem can be solved by sensing and modeling uncertain spatial behaviors, defining topological rules, and using inference and learning capabilities in a spatial reasoning system. Reasoning, which is the main component of such complex systems, requires that proper rules be defined through expert judgments in the knowledge-based part. Use of genetic fuzzy capabilities enables the algorithm to learn and be tuned to proper rules in a flexible manner and increases the preciseness and robustness of operations. The main objective of this paper was to design and evaluate a spatial genetic fuzzy system, with the goal of assessing environmental risks of air pollution due to oil well fires during the Persian Gulf War. Dynamic areas were extracted and monitored through images from NOAA, and the data were stored in an efficient spatial database. Initial spatial knowledge was determined by expert consideration of the application characteristics, and the inference engine was performed with genetic learning (GL) algorithms. Finally, GL (0.7 and 0.03), GL (0.7 and 0.08), GL (0.98 and 0.03), GL (0.98 and 0.08), and Cordon learning methods were evaluated with test and training data related to samples extracted from Landsat thematic mapper satellite images. Results of the implementation showed that GL (0.98, 0.03) was more precise than the other methods for learning and tuning rules in the concerned application.