基于粗集的环境机制发现模型及其渔业应用

地学事件或地学变量受控于环境因子,其关系常为非线性。另一方面,影响变量取值或事件发生的时空范围及其环境要素具有不确定性。环境因子的时空配置关系集中体现这种关系的复杂度。这使得寻找决定事件发生或某些地学变量取值的环境因子及其组合存在困难。针对渔场形成的环境机制发现,构建RS-STAMM模型,将时空离散化,以邻域方法提取空间环境变量,形成决策表,利用粗集约简方法,对环境因子及其时空配置关系进行筛选,进而寻找影响事件或变量取值的环境因子的时空配置结构。最后以发现渔场形成的环境机制为目标,将模型应用在渔业遥感研究中,以海洋鱼类聚集的温度场配置提取为实例,验证模型有效性。

Rough-Set-Based Spatiotemporal Assignment Mining Model with Its Application for Marine Fishery

Geo-event is controlled by the environment factors with nonlinear relationship. That means it is important to discover the spatiotemporal assignment of environmental factors. Vector-based association rule discovery models have been provided to look for environmental pattern successfully in terrestrial applications recently. But they just consider the topological relationship in static state between parcels or objects with complex algorithm. And the data type of the attribution has to be limited as category. Except the topologic relationship, it is difficult to take other relationships into account. In the environment research, the continue field also is important research content, especially in meteorology and oceanography. And the continue field always includes the temporal property as geographic phenomenon or geographic progress. This work is to deal with the process. It disperses the space with time. A neighborhood is defined to extract the spatiotemporal variables to make a decision table. A reduction algorithm based on rough set will mine the spatiotemporal assignment with the environmental factors. Finally, the model was applied in fishery geography to find the environmental pattern, which determines the form of fishing ground.