## 3 Results
This indicates that the EMM can respond quickly, making it suitable for applications requiring timely predictions;
* Space Complexity Measurement was employed using the GC.GetTotalMemory() method evaluated memory usage before and after significant operations in the code. This helped quantify the EMM's memory footprint during its execution phases. During its operations, the EMM utilized approximately 1,610,984 bytes (or around 1.6 MB) of memory. This memory consumption is manageable for many resource-constrained devices, particularly those commonly used in IoT applications, including training and evaluation resources. The inclusion of this experimental evaluation of time and space complexity clarifies the EMM's feasibility for deployment in real-time monitoring systems, particularly in the context of earthquake prediction. The following four possible scenarios are presented in Table 4 by applying Equation (1). The calculated \(P(EQ)\) values for each scenario (0.926, 0.704, 0.31, and 0.13) from Table 4 reflect the combined influence of the probability of earthquake occurrence and the real-time sensor data provided by the network of devices. High values (0.926, 0.704) suggest a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & \(P_{\text{EMM}}\) & \(N\) & \(\sum\)**S\({}_{i}\)** & \(P(EQ)\) & **Description** \\ \hline \(1\) & 0.92 & 10 & 8 & \(P(EQ)=0.7\times 0.92+0.3\times 0.8=0.88\) & The probability of event occurrence is 0.98, and 8 out of 10 devices report exceeding & The probability of event occurrence is 0.92, and 2 out of 10 devices report exceeding & The probability of event occurrence is 0.10, and 8 out of 10 devices report exceeding \\ \hline \(2\) & 0.92 & 10 & 2 & \(P(EQ)=0.7\times 0.92+0.3\times 0.2=0.70\) & The probability of event occurrence is 0.92, and 2 out of 10 devices report exceeding & The probability of event occurrence is 0.10, and 8 out of 10 devices report exceeding \\ \hline \(3\) & 0.10 & 10 & 8 & \(P(EQ)=0.7\times 0.10+0.3\times 0.8=0.31\) & The probability of event occurrence is 0.10, and 2 out of 10 devices report exceeding \\ \hline \(4\) & 0.10 & 10 & 2 & \(P(EQ)=0.7\times 0.10+0.3\times 0.2=0.13\) & The probability of event occurrence is 0.10, and 2 out of 10 devices report exceeding & The probability of event occurrence is 0.10, and 2 out of 10 devices report exceeding \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computing the \(P(EQ)\) in four scenarios based on Equation (1). probability of an earthquake driven by the reactive and predictive components. Lower values (0.31, 0.13) reflect scenarios where the probability is low, especially when either the event model prediction or the sensor data are weak, indicating no immediate risk. Equation (2) is used to identify the equipment for which data are analyzed, as illustrated in the example calculation below. The method for converting latitude and longitude coordinates for Vrancea (45.8 north latitude and 27.2 east longitude) and Bucharest (44.2 north latitude and 26.1 east longitude) to determine the distance in kilometers using Equation (2) is described as follows:  
1. Converting the latitude and longitude from degrees to radians, using Equation (3):  
* Conversion for Vrancea latitude and longitude: \[\phi_{1}=\frac{45.8\times\pi}{180}=0.798\ radians\] \[\lambda_{1}=\frac{27.2\times\pi}{180}=0.474\ radians\]
* Conversion for Bucharest latitude and longitude: \[\phi_{2}=\frac{44.2\times\pi}{180}=0.776\ radians\] \[\lambda_{2}=\frac{26.1\times\pi}{180}=0.456\ radians\]
2. Calculating the differences between the coordinates: \[\Delta\phi=\phi_{2}-\phi_{1}=-0.022\ radians\] \[\Delta\lambda=\lambda_{2}-\lambda_{1}=-0.018\ radians\]
3. Computing the Haversine of \(\Delta\phi\) and \(\Delta\lambda\) using Equation (4): \[haversin(-0.022)=sin^{2}(-0.011)\cong 0.000121\] \[haversin(-0.018)=sin^{2}(-0.009)\cong 0.000081\]
4.