## 3 Experimental Scenarios
### Scenario I: Experimental Results
A comparative plot of the RMSE values was constructed to visually assess the relative performance of the two architectures across different input-output configurations. Figure 8 illustrates how the prediction error, as measured by RMSE, evolved for each configuration (1 through 4, meaning the four different input and output windows discussed previously) for both the feedforward slideNN and the recurrent GRU model. Each point on the curves corresponds to a specific model setup, with the x-axis representing increasing input and output size and the y-axis showing the corresponding RMSE. This comparison highlighted the general trend of the performance improvement in both models as the amount of historical data increased while also showcasing the consistent superiority of the GRU architecture in minimizing prediction error across all scenarios. As shown in Figure 8, the GRU models outperformed all slideNN models in terms of RMSE when using the same dataset, data transformations, and training parameters. For Configuration 1 of 8 of the vectorized timestep inputs of environmental measurements (\(T,H,W_{s},Wd,PM1,PM2.5,PM4\), and \(PM10\)), the stranded GRU model presented 25% less loss than the slideNN model. A similar profile was also maintained for Configuration 2 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **RMSE** & **MSE** & **\(p\)-Value** & **Effect Size** \\
**(Input \(\rightarrow\) Output)** & **(Mean \(\pm\)\(\sigma\))** & **(Mean \(\pm\)\(\sigma\))** & **(vs. 64\(\rightarrow\)2)** & **(Cohen’s d)** \\ \hline \(64\to 2\) & 0.599 \(\pm\) 0.042 & 0.369 \(\pm\) 0.038 & – & – \\ \(128\to 4\) & 0.507 \(\pm\) 0.028 & 0.262 \(\pm\) 0.022 & 0.028 & 0.85 \\ \(256\to 8\) & 0.317 \(\pm\) 0.015 & 0.101 \(\pm\) 0.008 & 0.022 & 1.65 \\ \(512\to 16\) & 0.233 \(\pm\) 0.011 & 0.055 \(\pm\) 0.004 & 0.020 & 1.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The conservative performance measures for the GRU model in edge computing (mean \(\pm\) std over 10 runs). Figure 8: The RMSE values (AQI units) obtained from each of the four input–output configurations on both edge computing model architectures: slideNN and GRU. (strand), which had 16 timestep inputs. Then, for the 32 and 64 temporal input configurations, the GRU models outperformed even the slideNN models, offering 50% and 80% less loss, respectively. Furthermore, to achieve the good aforementioned slideNN losses (expressed by RMSE), the model was trained over 400 epochs concerning the GRU models' 50 epochs, which was achieved using a stop training condition of three epochs patience and a delta value to qualify as an improvement of 0.001. This indicates that GRU models can distinguish temporal patterns more effectively than plain NN models and train significantly faster than NN models. Regarding dataset training epochs, the GRU model training was at least eight times faster compared to slideNN. In conclusion, under similar-sized models with the same number of parameters and memory sizes, the GRU models performed at least 25% better than the NN models for small temporal timesteps and at least 50% better for medium temporal timesteps. Looking at the inference times, both models performed similarly in their corresponding configurations, showing no significant delays (similar inference times).