## 4 Results and Discussion  
Upon the completion of the methodology, the outputs consist of various forecasting systems resulting from the combinations of each MF with each defuzzification method. Consequently, a data synthesis approach was proposed to demonstrate the accuracy of each system. Accordingly, parallel coordinate plots were utilized to perform this synthesis; this tool possesses several advantages, including result integrity, connectivity of results, and the significance of the results. The latter advantage bolstered this evaluation, as the addressed dimensions are essential for data exploration [31]. The fuzzy systems were analyzed in accordance with the process of the association mining rules (see Algorithms 1 and 2). For the initial group, Figure 5 illustrates the forecasts produced by the fuzzy systems based on Rule 1, derived from the association rule mining of PM\({}_{2.5}\). Additionally, Figure 6 depicts the precision of Rule 2 concerning O\({}_{3}\). The accuracy of the forecasting systems was quantified using the MAPE. In group one of the systems, an important finding is illustrated in Figure 5, which emphasizes the features of the defuzzification method. Consequently, the LOM approach proves inadequate when applied to two MFs (trapezoidal and Gaussian), resulting in the lowest forecasting accuracy. Conversely, it is noteworthy that combining the SOM with a triangular MF yields significantly improved performance results, thereby enhancing the systems' accuracy. When a fuzzy system employs fundamental conditional rules, it is imperative to integrate triangular or Gaussian MFs within its architecture and to combine these with the methods of defuzzification known as the MOM or SOM. Both approaches yield comparable accuracy metrics. The approximation of reasoning enables the achievement of uncertain outcomes contingent upon the precision of the fuzzy system [32]. In addition to the aforementioned results, Figure 6 was constructed based on the conjunctive rules, with an additional grade of complexity incorporated. Moreover, evaluating and integrating the SOM methodologies in complex systems is of considerable relevance. In this context, all the MFs significantly enhanced the prediction accuracy when applying the SOM method Concurrently, the LOM defuzzification technique illustrated the prediction loss when utilized. The outcomes produced, in conjunction with the trapezoidal and Gaussian MFs, yielded lower values of the MAPE, thereby clarifying the appropriate core functions to be implemented in such systems. In this context, the defuzzification method is paramount in enhancing the design outcomes of an air pollution behavior system, which is explicitly based on the SO\({}_{2}\), PM\({}_{2.5}\), temperature, wind speed, and O\({}_{3}\) variables. The findings presented in Figures 5 and 6 support this assertion. This represents a pertinent conclusion regarding the defuzzification method due to the absence of non-interval data, whereby there is no significant variation in the results across different defuzzification methods [33]. Fuzzy systems are being developed to address various industrial domains, including power compression, valve actuation, temperature escalation, humidity enhancement, air propagation angles, and motor damper voltage [34]. Additionally, environmental systems, encompassing ecological impacts, water quality indices, renewable energy, and the forecasting of material concentrations, are also addressed by fuzzy systems [35; 36]. For numerical values, a study has investigated the application of fuzzy logic in a load-balancing scenario to mitigate uncertainty in decentralized decision making, utilizing the states of all nodes to ascertain the global condition of the system. In this context, the defuzzification method is based on the following assumptions:  
Figure 5: The accuracy of systems in forecasting PM\({}_{2.5}\) through a conditional rule (Rule 1). The lowest MAPE values showed better accuracy in their forecasting task.