## 3 Results  
The ML task chosen for this experiment was regression, aimed at predicting the magnitude of seismic events. The training process explored different models by varying the training time [73; 74; 75; 76]:  
FastForestRegression uses the FastForest method that combines multiple decision trees to improve prediction accuracy. It is beneficial for handling complex, nonlinear data relationships, like those seen in environmental data, and for predicting factors like temperature or seismic activity. This algorithm is based on a random forest approach, which captures diverse patterns by training on various data subsets;  
FastTreeRegression uses the FastTree gradient-boosted decision tree algorithm. It builds multiple trees in sequence, with each tree correcting errors from the previous one. This method is well-suited for real-time applications where predicting environmental trends or sensor anomalies needs large datasets;  
FastTreeTweedieRegression is a variation of FastTree. It is optimized for Tweedie distributions and useful in scenarios with zeros and positive values, such as rainfall or environmental pollutant levels. It is particularly applicable to IoT data where events or measurements may be infrequent but impactful;  
LbfgsPoissonRegression is a version of Poisson regression that uses the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) optimization algorithm. It is ideal for counting data or events occurring over time, making it helpful in predicting the number of environmental events (like minor seismic activities) based on sensor data patterns;  
Figure 5: Time-series plot of acceleration data captured by the MPU-6050 sensor over a 6.4-s timeframe at a 100 Hz sampling rate. LightGbmRegression (Light Gradient Boosting Machine) is a highly efficient gradient-boosting framework based on decision trees. It is designed for quick training and low memory usage, ideal for handling large-scale IoT data streams, such as those from multiple environmental sensors, and is effective in modeling complex relationships for accurate predictions;  
SdcaRegression (Stochastic Dual Coordinate Ascent--SDCA) regression is a linear regression algorithm that uses a stochastic optimization approach, making it suitable for fast, scalable training on large datasets. In the context of IoT data, it could be applied to model straightforward relationships in large volumes of environmental data where high-dimensional input features are present;  
Each method is implemented in MLNET, often used to train predictive models based on historical data to forecast future sensor readings or environmental trends. Multiple tests were performed to identify the most suitable method for predicting seismic activity. The goal of the regression task was to minimize errors and optimize performance using a suitable model. The following key metrics measured the performance of the evaluated models [77]:  
* R-squared (R\({}^{2}\)) is a metric that indicates the proportion of variance explained by the model. Higher values, closer to 1, suggest a better fit;
* Absolute Loss (MAE) is the average absolute difference between the predicted values and the actual values;
* Squared Loss (MSE) is defined as the average of the squared differences between predicted values and actual values;
* Root Mean Square Loss (RMS) is the square root of the average squared differences, reflecting how well the model predicts magnitude;
* Duration means the time taken by each model during its evaluation. EMM model tests were performed under varying conditions. For the analysis, the data generated by the best-performing algorithm identified during the study were used as a benchmark, with a training duration of 600 s:  
* Test 1 (Average of parameters for the last 30 Days). This test calculated the mean values of the features used for the model over the most recent 30-day period, providing a broad view of the parameter trends;
* Test 2 (Average of parameters for the last 15 days).