## 4 Discussion  
The study's results suggest that integrating meteorological parameters into earthquake prediction models can provide a relationship among factors such as precipitation, surface pressure, air temperature, air relative humidity, and seismic events. The predictive models demonstrated a 99.84% accuracy in forecasting seismic activity with the classification approach FastTreeOva algorithm. The successful development and validation of the IoT-based alerting system indicate that real-time monitoring, combined with predictive analytics, can enhance early warning capabilities based on a proposed mathematical formalism. The findings of this study have significant implications for improving earthquake preparedness and risk mitigation. By integrating weather data into predictive models, we open new avenues for understanding potential precursors to seismic activity. The development of a real-time IoT-based alerting system offers practical applications for public safety, enabling faster warnings that could potentially save lives. While the current study focuses primarily on exploring the predictive capacity of integrating meteorological and seismic data using a custom ML approach, we acknowledge that a rigorous comparison with conventional and recent predictive models would provide valuable insights into the model's relative effectiveness. Given our dataset's unique structure and region-specific focus, an experimental evaluation with models widely applied in earthquake prediction, such as statistical or physical models based on seismic precursors, will be considered in future work. This comparison could further highlight the strengths and limitations of our approach in terms of accuracy. The proposed method for generating adversarial examples [78] can be linked to earthquake prediction models by improving their ability to handle potential adversarial attacks. Figure 7: Seismic activity: (**a**) Detection outside the normal range, triggering an alert; (**b**) Stable readings within the normal range, indicating no seismic activity. Ensuring that models accurately interpret sensor data without being misled by altered inputs impacts reliable predictions in seismic data analysis. By examining how adversarial examples impact prediction models, researchers can increase the performance of earthquake forecasting systems, which is important for timely alerts and disaster preparedness. Future research could explore defenses against such adversarial techniques in seismic applications, ensuring that models maintain accuracy where data integrity is important in real-world scenarios. Integrating more extensive datasets and advanced ML techniques is a potential area for further exploration, thereby contributing to the ongoing development of predictive analytics in seismology. The earthquake prediction model introduced in this study has limitations that must be acknowledged for future research. One fundamental limitation is the model's geographical focus on the Vrancea region, which raises questions about its applicability to other seismic areas with different geological characteristics. As the authors desire to expand the model's reach, applying the process to other seismic centers becomes a primary goal. This expansion would involve validating the model's performance in varied seismic contexts and understanding how geological factors influence its predictive capabilities. The authors aim to install real-time monitoring equipment in multiple cities located at considerable distances from the Vrancea region. However, such implementation poses logistical challenges, including maintenance of the monitoring devices. By applying their earthquake prediction process to other seismic centers and implementing real-time monitoring in multiple cities, they hope to significantly enhance the model's performance and provide a more comprehensive understanding of seismic activity across diverse geographical landscapes. Another limitation of the research is the potential correlation between meteorological conditions and seismic activity.