Explanation of Air Quality Data Using Takagi–Sugeno Fuzzy Inference System
Alžbeta Michalíková · Applied Sciences · 2025
The explainability of system behaviour is one of the most important concepts of modern data science. If a system is described by using rules that are clearly readable and understandable, then it is possible to model various problems arising from real life. In this paper, we present a way to create the so-called IF-THEN rules for urban air quality modelling by using the Takagi–Sugeno fuzzy inference system. The presented research study builds on previous work where such a problem was modelled by using a Takagi–Sugeno fuzzy inference system with linear membership functions. Such functions are difficult for the average person to interpret. Therefore, we replaced the output linear functions with constant functions and subsequently optimised the system to achieve the lowest approximation error. From the point of view of data analysis, this approach allows us to obtain a system with a comparatively smaller approximation error. From the point of view of model explainability, we obtain a rule base that describes the influence of individual input variables on the overall output in human terms. Finally, based on the obtained rules, we can evaluate the impact of traffic data and weather conditions on the selected air pollution parameter.