## 2 Materials and Methods
### ML Building Model  
This section details the construction of the predictive Earthquake Magnitude Machine (EMM) model. The implementation utilized the MLNET framework for local training. This process was executed on a system equipped with an 11 th Gen Intel(r) Core(tm) i5-113G7 processor and 8 GB of RAM. From the total dataset of 8766 records, 70% were allocated for training purposes, while the remaining 30% were designated for validation to assess the model's accuracy. To maintain the rigor of the results, 70% of the records marking earthquake occurrences and 70% of the records without earthquakes were extracted. This approach ensures that the testing process is balanced and representative of both scenarios, allowing the model to accurately learn the characteristics associated with seismic events and those of non-seismic conditions. By using stratified sampling, we can improve the model's ability to distinguish between the two classes, thus enhancing its predictive performance and minimizing bias. The model's performance is continuously validated using the 30% validation set, ensuring that any issues related to overfitting or underfitting could be addressed directly in this phase. Given the limited size of our dataset and the need to maximize data utilization for model training and validation, a separate test set was unnecessary for this study. The data used after 25 September 2024 were utilized for model testing, and the number of days is very small compared to the training volume, providing a limited testing scale. The authors acknowledge that, at this moment, the model does not provide clear information regarding the testing phase. Further validation and refinement are necessary to enhance its accuracy in future research. MLNET is a machine learning framework developed by Microsoft. It is designed to integrate ML components into.NET applications. It allows developers to create models for various tasks, including classification, regression, and clustering, without requiring extensive knowledge of ML algorithms [71]. After training, the best performance-tested algorithm is automatically chosen. The ML model requires features and labels for training:  
* Features are the input variables used by the model to make predictions. They represent the data characteristics the model will analyze to identify patterns and relationships. For the EMM model, the features include Temperature, Pressure, Precipitation, and Humidity. These features provide the model with the necessary environmental context to understand the conditions under which earthquakes occur or do not occur. * Labels are the target variables the model aims to predict based on the provided features. For the EMM model, the label is Magnitude. In cases where no earthquake occurred, this value is set to zero. The EMM model is a single-output regression model that forecasts a continuous numerical value based on input features. It predicts the magnitude of earthquakes based on provided environmental features such as precipitation, surface pressure, air temperature, and air relative humidity. Since the model has only one output (the magnitude of an earthquake), it focuses on predicting this specific value rather than multiple outputs simultaneously. Predicting magnitude, a continuous value, classifies this as a regression task. The goal is to learn the relationship between the input features and the target variable (magnitude) through the training process. The EMM model utilizes input features to derive meaningful predictions about the output variable. The research evaluates multiple approaches using various ML models and performance metrics to assess which method yields better accuracy results. The study presentstwo scenarios: value prediction and data classification. The features are extracted for each scenario based on different ideas, primarily identifying the best method. We construct two SQL queries to extract the required data from the SQL database. The first will select 70% of the records where Magnitude > 0, and the second will choose 70% where Magnitude = 0.