Geoevent association rule discovery model based on rough set with marine fishery application

Vector-based association rule discovery models have been provided to look for spatial knowledge in terrestrial applications recently. Most of them just consider the attribution relationship of local space or the topological relationship between parcels or objects. And they are difficult to consider the temporal change, distance relationship or direction relationship. In this presentation, an geoevent association rule discovery model (GEARDM) is developed, which is based on the spatiotemporal grid and rough set, and the experiment in marine fishery application is provided in support of the model. In GEARDM, the continuous spatiotemporal process is discretized by fuzzy preknowledge as spatiotemporal assignment and will be assigned into a decision table. The mining algorithm based on rough set results from the decision table in the geoevent association rules, which shows what kind of pattern of spatiotemporal assignment of environmental factors determine the happen or attribution of the geoevents. After the mined rules being analyzed by knowledge, the refined knowledge is obtained and the spatiotemporal assignment will be renewed. Then the flow of GEARDM feeds back to the phase of discretization and iterates until the final rules are reasonable. These knowledge-based rules also can be used in the expert system to predict the happen or the scale of geoevent as the experiments show.