## 3 Methodology
### Air Quality Data for Systems Evaluation  
At this stage, the descriptive values of the collected data are presented, because these values are utilized in the subsequent two sections (a) to elucidate an issue concerning the generation of the fuzzy sets, as detailed in Section 3.4.1, and (b) to assess the evalu  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{2}{c}{**PM\({}_{2.5}\) (ug/m\({}^{3}\))**} & \multicolumn{1}{c}{**O\({}_{3}\) (ppm)**} & \multicolumn{1}{c}{**CO (ppm)**} & \multicolumn{1}{c}{**SO\({}_{2}\) (ppb)**} & \multicolumn{1}{c}{**NO\({}_{2}\) (ppb)**} \\ \hline Good & [0.0, 12.0] & [0.0, 0.054] & [0.0, 4.4] & [0.0, 35.0] & [0.0, 53.0] \\ \hline Moderate & [12.1, 35.4] & [0.055, 0.070] & [4.5, 9.4] & [36.0, 75.0] & [54.0, 100.0] \\ \hline Unhealthy (groups) & [35.5, 55.4] & [0.071, 0.085] & [9.5, 12.4] & [76.0, 185.0] & [101.0, 360.0] \\ \hline Unhealthy & [55.5, 150.4] & [0.086, 0.105] & [12.5, 15.4] & [186.0, 304.0] & [361.0, 649.0] \\ \hline Very unhealthy & [150.5, 250.4] & [0.106, 0.200] & [15.5, 30.4] & [305.0, 604.0] & [650.0, 1249.0] \\ \hline Hazardous & [250.5, 500.4] & [0.201, 0.305] & [30.5, 50.4] & [605.0, 1004.0] & [1250.0, 2049.0] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linguistic terms for air quality variables.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & \multicolumn{2}{c}{**Air Temperature (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**Wind Speed (km/h)**} \\ \hline Sharply cold & [0.0, 0.9] & Calm & [0.0, 0.9] \\  & & Light air & [1.0, 5.0] \\ Cold & [1.0, 8.0] & Light breeze & [6.0, 11.0] \\  & & Gentle breeze & [12.0, 19.0] \\  & & Moderate breeze & [20.0, 28.0] \\ Moderately cold & [9.0, 16.0] & Fresh breeze & [29.0, 38.0] \\  & & Strong breeze & [39.0, 49.0] \\ Comfortable & [17.0, 22.0] & Near gale & [50.0, 61.0] \\ Warm & [23.0, 27.0] & Gale & [62.0, 74.0] \\  & & Strong gale & [75.0, 88.0] \\  & & Storm & [89.0, 102.0] \\ Hot & [28.0, 42.0] & Violent storm & [103.0, 117.0] \\  & & Hurricane & [118.0, 150.0] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linguistic terms for meteorological variables.  
ation method of the system's accuracy in Section 3.7. In total, 52,560 data points were collected in 2021 in Cuenca, Ecuador, which has relatively satisfactory AQ. Nevertheless, the municipality consistently monitors the AQ and meteorological variables to alert the population regarding significant changes. An example of the data is available on GitHub ([[https://github.com/LIDI-UDA/airfuzzy](https://github.com/LIDI-UDA/airfuzzy)]([https://github.com/LIDI-UDA/airfuzzy](https://github.com/LIDI-UDA/airfuzzy)), accessed on 16 December 2024).  
In the initial phase of data preprocessing, data conversion to standard international units of measurement was executed. Subsequently, due to the satisfactory AQ of the variables within the city, a substantial number of values exist within the lower intervals, namely the good, moderate, and unhealthy groups. Consequently, the subsequent task involved scaling these values to align with the minimum and maximum values of the standardized metrics delineated in Tables 1 and 2, aiming to augment the sample size for each interval of the linguistic terms while preserving the original dispersion characteristics of the data. The descriptive statistics of the variables are presented in Table 3, facilitating the identification of the behavior of the input data.