# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 5 Conclusions  
This research proposes novel features for the _AQI_ model and sets out to develop a system for predicting and forecasting AQ by integrating ML and DL models into an IoT infrastructure. The main research question focused on whether a customized approach to predictive modeling could improve AQ monitoring and forecasting accuracy. Developing and deploying the MA-AQI and AQIF models demonstrated that combining tailored algorithms improves AQ prediction and future trend forecasting. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Analyzed Parameters** & \(R^{2}\) **(\%)** & **Reference** \\ \hline
8 & 71.62 & AQIF \\
4 & 82.76 & [97] \\
2 & 79 & [98] \\
1 & \textless{}80 & [99] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of analyzed parameters and \(R^{2}\) value for AQIF and other studies. The MA-AQI model achieved a high \(OP\) of 95.61%, providing a reliable solution for real-time AQ monitoring. The AQIF model, while achieving a lower \(R^{2}\) accuracy of 71.62%, demonstrated its potential for longer-term forecasting. Integrating these models into the RTPF platform, combined with the PIP component for decision-making, highlights the practical application of our system in real-world scenarios, enabling both authorities and the public to take informed actions. Two informative messages were integrated using Azure Communication Service, for the interval between 101 and 150 sending a warning email message and for the \(AQI\) interval between 151 and 200 sending an SMS email alert. Our PIP alert system stands out by generating notifications based on \(AQI\)-specific measurements and incorporating additional parameter readings that influence air quality. Furthermore, it provides warning alerts based on forecasting predictions, a capability that current alert systems lack, making our solution proactive. Reflecting on the research process, the study successfully addressed the challenges of integrating complex ML and DL models into an IoT framework. However, further work is needed to improve the accuracy of the AQIF model and extend data analysis to another industrial area. The study presents advancements in \(AQI\) monitoring and prediction, integrating data analysis, ML, DL, and IoT framework for a comprehensive approach to AQ management. The research begins with the import and analysis of AQ data, aimed at identifying parameters involved in the AQ monitoring. A core contribution is the analysis and development of six customized ML algorithms specifically designed to address the unique features of AQ data and improve predictive accuracy. Additionally, six pre-existing ML models were seamlessly integrated using ML.NET, enriching the predictive framework with diverse algorithmic approaches. To further bolster prediction capabilities, the study implemented and incorporated seven customized DL algorithms tailored for AQ forecasting, accounting for AQ data's intricate patterns and time dependencies. In addition to model development, the study offers a thorough comparative analysis of ML and DL approaches. By introducing a novel performance metric proposed by the authors, the analysis reveals the strengths and limitations of each methodology, identifying the optimal strategy based on key performance indicators. Another notable aspect of the study is integrating a dual-model forecasting system, which allows for adaptable and accurate AQ predictions over variable timeframes. This model flexibility is complemented by embedding the predictive framework within an IoT infrastructure for real-time data integration and AQ monitoring. This study developed a user-friendly application incorporating predictive and forecasting capabilities, making AQ insights readily accessible for end-users. Finally, a comprehensive decision-making framework was proposed, leveraging Azure Communication Services to drive AQ management actions.