# Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)
## 5 Conclusions  
This article contributes to the advancement of methods for implementing an Operational Earthquake Forecasting (OEF) system by presenting a novel solution. Several key conclusions can be drawn from the development of the OEF-NIEP system:  
* The structure of the OEF-NIEP system is innovative compared to existing approaches. We found no other solutions that utilize real-time event detection--such as threshold exceedance adapted to seasonal variations in gas emissions--directly at monitoring stations within a network and integrate these into a decision-making matrix. * Experimental results confirmed that the Gutenberg-Richter law (Equation (5)) and gas emissions (radon, CO2) can be used for real-time earthquake forecasting. This enables the possibility of a service where authorities can receive warning information within a time window ranging from several hours to a few days. * The RTM 1688 radon and thoron monitoring equipment installed at Plotstina and Vrancioaia did not improve earthquake forecasting performance compared to the existing Radon Scout Plus equipment. The role of thoron as a precursor at these monitored locations remains unproven, though seismic activity in the area has not exceeded magnitude 4.5. * Years of experience with real-time monitoring suggest that simple, flexible, and cost-effective solutions (including equipment, maintenance, and analysis software) significantly support the advancement of fundamental research through applied research. However, increasing system complexity presents challenges in terms of implementation, maintenance, and funding. More accurate analyses, such as those based on artificial intelligence algorithms, can be developed and applied offline. * When developing the OEF system, it is essential to include additional factors beyond radon and CO2, and to increase the number of monitoring stations to allow for more accurate localization of future earthquakes. * Environmental factors (air pressure, temperature, humidity) can influence gas emissions and may trigger false alarms. Therefore, to mitigate this, sensors should be installed at greater depths. * Another important conclusion is the need to incorporate the findings of the AFROS project's fundamental research into the real-time decision matrix, including advanced statistical and machine-learning tools for quantifying spatial and temporal features. * The article emphasizes decision-making based on real-time data. Anomaly detection was performed using thresholds determined by offline analyses; these thresholds should be further developed and integrated into the OEF system. * The monitored area should be expanded to include other seismic zones (e.g., Targu Jiu, Izvoarele-Galati). Some of these regions are characterized by oil and gas extraction, necessitating the analysis of induced seismicity. * Only a multidisciplinary approach can yield significant results in earthquake prediction. The existence of a platform like AFROS, which presents and analyzes multidisciplinary data in real time, is unprecedented (no comparable systems were identified during our research) and demonstrates the feasibility of implementing an OEF system and providing a complementary service of REWS. Fundamental research in the field of earthquake prediction is supported by numerous articles. The literature review in this paper shows that the implementation of theories in applied research takes place over short periods of time, as long as project funding is available. In conclusion, this article presents a method for earthquake prediction based on real-time radon and CO\({}_{2}\) data that can be visualized using the AFROS platform (we have not encountered such platforms before) and requires further development. Artificial intelligence (AI) can generate numerous analytical methods, but without practical implementation, they remain purely theoretical.