A rule-based intuitive reasoning scheme for exploring a large weather database
Yaru Sun, Robert Kok · 2008
Human intuition is an inherent mental ability that has the capacity to deal with complex situations when the knowledge available is fuzzy, uncertainty, and often inconsistent. The overall objective of this work is to construct an inference engine that reasons about complex problems in a basic intuitive manner. A large weather database consisting of 54 variables and 50 years of hourly records was acquired to represent the complex situation. This database was found suitable due to its high dimensionality and the non-linear relationships among the variables. A rule-based reasoning scheme was used, with rules being derived by Papers presented before CSBE/SCGAB meetings are considered the property of the Society. In general, the Society reserves the right of first publication of such papers, in complete form; however, CSBE/SCGAB has no objections to publication, in condensed form, with credit to the Society and the author, in other publications prior to use in Society publications. Permission to publish a paper in full may be requested from the CSBE/SCGAB Secretary, PO Box 23101, RPO McGillivray, Winnipeg MB R3T 5S3 or contact [email protected]. The Society is not responsible for statements or opinions advanced in papers or discussions at its meetings. drawing numerous local, low certainty conclusions from small chunks of data rather than obtaining relatively fewer, global, high-certainty rules by analyzing the whole database at once. The task of the reasoning engine is to predict certain weather events by examining the body of low-quality rules and integrating corroborating evidence from them to obtain high-certainty conclusions. Two types of reasoning were tested: fast and broad. The fast reasoning mimics split-second insights and the broad reasoning represents deeper background reflection. The reasoning results are compared to those of a typical environmental data analysis approach.