# Predicting the Magnitude of Earthquakes Using Grammatical Evolution
## 4 Conclusions  
This article presents an innovative approach to predicting earthquake magnitudes using the Grammatical Evolution method, which offers significant advantages over traditional machine learning techniques. Experimental results for the years 2004, 2010, and 2012 demonstrate that models employing Grammatical Evolution (RULE, NNC, FC) consistently achieve lower and more stable regression errors compared to models without Grammatical Evolution (MLP(BP), MLP(RPROP), MLP(BFGS)). The superiority of these models is evident both in their reduction of the mean error and in their resilience to changes in distance, indicating better adaptability to varying spatial conditions. The study's conclusions emphasize the capability of Grammatical Evolution to effectively identify and exploit structures and relationships in seismic data. Despite the observed improvement in non-Grammatical Evolution models over time, their performance remained inferior to that of models incorporating Grammatical Evolution. This demonstrates that using Grammatical Evolution provides more reliable predictions, a critical factor for applications such as earthquake early-warning systems.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Noise Percent**} \\ \hline
**Feature** & **0.1\%** & **0.05\%** & **1\%** & **2\%** & **5\%** \\ \hline
1 & 0.176 & **0.228** & 0.175 & 0.175 & 0.175 \\ \hline
2 & **0.177** & 0.175  
As future directions, this study suggests exploring the exact mechanisms through which Grammatical Evolution enhances performance, focusing on analyzing the structure of the generated rules. Additionally, the scalability of the method to larger and more complex datasets could be investigated, as well as its comparison with other advanced machine learning techniques. Another avenue could involve optimizing the method's hyperparameters and incorporating additional geophysical parameters for even more accurate predictions. Finally, the real-time application of the model and its integration into early-warning frameworks could be significant steps toward the practical utilization of this study's findings.  
Furthermore, as previously mentioned in the introduction, several developments of various models have focused on short-term early-warning systems, typically offering a lead time of approximately one minute, based on real-time detection of tectonic or lithospheric plate movement as it occurs. In contrast, our study not only targeted seismic events of magnitude 5.0 and above, but also took a step further by demonstrating the potential to predict such events before any plate movement is detected. Consequently, our model can potentially be integrated into a broader early-warning framework capable of delivering mid-to-long-term predictions of earthquake occurrences, ranging from hours or days to even months in advance, thereby offering a significantly enhanced preparedness window and risk mitigation efforts.  
[PERSON], V.C. and I.G.T. conceived of the idea and the methodology, and C.K. and V.C. implemented the corresponding software. C.K. conducted the experiments, employing objective functions as test cases, and provided the comparative experiments. V.C. performed the necessary statistical tests. All authors have read and agreed to the published version of the manuscript.  
This research has been financed by the European Union: Next Generation EU through the Program Greece 2.0 National Recovery and Resilience Plan, under the call RESEARCH-CREATE-INNOVATE, project name \"iCREW: Intelligent small craft simulator for advanced crew training using Virtual Reality techniques\" (project code: TAEDK-06195).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.