# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
## 5 Conclusions  
Air pollution is a global issue that affects countries and regions around the world. It has wide-ranging impacts on various aspects of the environment, public health, and the global climate system. This problem has been further aggravated due to the increase in the global population, urbanization, industrialization, and climate change. There is an urgent need to provide precise predictions of air pollution levels, which will contribute to improving public health and overall quality of life. The application of ML algorithms provides promising results for CAQI prediction. The focus of this paper was to propose an advanced hybrid ML model for hourly CAQI prediction in the region of Niksic, Montenegro. The proposed hybrid LSTM model with bagging (i.e., the ensemble model) delivered significantly better performance when compared to the SVM model. A comprehensive statistical analysis was conducted on the calculated MAE values obtained from the training and testing of the LSTM model, the best LSTM model without bagging, and the ensemble model. The results showed that the ensemble model outperformed the other two compared models. This novel hybrid model can be considered as a new and superior alternative for hourly CAQI prediction. The application of such advanced ML analysis using the hybrid approach has not been previously employed in the context of air pollution in Montenegro. Although our analysis showed that the ensemble model outperformed SVM as a technique to predict CAQI values, we draw the reader's attention to the fact that SVM \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Sample** & **Sum of Ranks** & **Mean of Ranks** & **Groups** \\ \hline \multirow{3}{*}{MAE training set} & Ensemble model & 107,205.000 & 357.350 & \(A\) \\  & Best LSTM without bagging & 126,283.000 & 420.943 & \(B\) \\  & LSTM & 171,962.000 & 573.207 & \(C\) \\ \hline \multirow{3}{*}{MAE testing set} & Ensemble model & 107,352.000 & 357.840 & \(A\) \\  & Best LSTM without bagging & 124,245.000 & 414.150 & \(B\) \\ \cline{1-1}  & LSTM & 173,853.000 & 579.510 & \(C\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multiple pairwise comparisons using the Steel–Dwass–Critchlow–Figner procedure. Two-tailed test on the calculated MAE values obtained from training and testing of the LSTM model, the best LSTM model without bagging, and the ensemble model. is still a promising technique that is worth considering. In particular, it has been shown in the literature that SVM is applicable to CAQI classification problems [29] as well as to regression problems [30], while, in some other papers [31], as in our analysis, SVM has shown slightly worse results. It is evident that the prediction of CAQI values is an extremely complex problem and that the performance of the applied algorithms depends significantly on the characteristics of the dataset; thus, it is important to apply various ML algorithms in modeling, without a priori assumptions that some are inefficient and inapplicable. The findings of this paper open up several possibilities for future research, such as exploring alternative optimization techniques, incorporating additional variables, considering long-term predictions, conducting comparative studies, and validating the proposed models in different regions, which could contribute to advancing the accuracy and applicability of air quality prediction models. Our future research will also explore the performance of the suggested model in the context of diverse data patterns. In particular, the potential of other metaheuristic algorithms can be investigated, as well as the hybridization of the existing techniques with additional optimization strategies. Future research could consider incorporating additional variables such as meteorological data (temperature, humidity, wind speed) to enhance the precision and robustness of the predictive models. Predictions with larger time steps, such as 8 h and 24 h predictions, can be considered as well.