Implementing an Intuitive Reasoner for Predicting Continuous Weather Variables

In this paper, the implementation of a rule-based intuitive reasoner is presented. The implementation included the rule induction module and the intuitive reasoner. A large weather database was acquired as the data source. Five weather variables from those data were chosen as the "target variables" whose values were predicted. A "complex" situation was simulated by making only subsets of the data available to the rule induction module. As a result, the rules induced were based on incomplete information with variable levels of certainty. Multiple linear regression was employed to induce rules from the data subsets. The intuitive reasoner was tested for its ability to use the induced rules to predict the values of the target variables. For reference, a weather data analysis approach which had been applied on similar tasks was adopted to analyze the complete database and create predictive models for the same five target variables. The intuitive reasoner showed potential by achieving prediction accuracy which compared favorably with that of the reference approach for two target variables, based on rules induced from only about 10% of the total data.