## 4 Model Training and Evaluation  
In our previous work [8], we used a supervector consisting of 15 physical sensor readings, each captured at 10-second intervals, to classify various environmental scenarios. The predictive modeling was carried out using a neural network, specifically a multilayer perceptron (MLP). The MLP architecture, known for its ability to capture nonlinear patterns and complex feature interactions, was trained on the dataset to achieve scenario-specific accuracies. While the model demonstrated reliable performance, its capability was inherently limited by the dimensionality and information content of the physical sensor data alone. Building on this foundational framework, we extended the supervector by integrating virtual sensor data generated through polynomial regression. These virtual sensors were designed to capture higher-order interactions and provide additional features to improve model performance. The study evaluates the MLP model's performance with three configurations of the augmented supervector:  
* **15 Physical Sensors + 15 Virtual Sensors**: Doubling the feature set with 15 derived features to assess the immediate impact of virtual sensor integration. * **15 Physical Sensors + 30 Virtual Sensors**: Further expanding the dataset to explore the effect of higher feature dimensionality. * **15 Physical Sensors + 45 Virtual Sensors**: A substantial augmentation to evaluate the potential diminishing returns of additional virtual sensors. The proposed work employs the MLP model for classifying forest fire scenarios based on sensor data. The model's input layer is designed to handle varying input configurations,depending on the number of physical and virtual sensors used. In the first case, where only physical sensors (PSs) are used, the input layer consists of 15 neurons corresponding to the 15 physical sensor features. For scenarios incorporating virtual sensors, the input layer expands to accommodate the additional virtual sensor data. Specifically, with 15 physical sensors and 15 virtual sensors (15 PS + 15 VS), the input layer has 30 neurons, while using 15 physical sensors and 30 virtual sensors (15 PS + 30 VS) increases the input layer to 45 neurons. Lastly, when 15 physical sensors and 45 virtual sensors (15 PS + 45 VS) are used, the input layer grows to 60 neurons. The model architecture includes two hidden layers with 64 and 32 neurons, respectively, and an output layer with 12 neurons to classify the 12 different fire scenarios. The MLP model is designed for classification, as indicated by the use of MLPClassifier, with regularization and early stopping to prevent overfitting. In this study, the dataset was split into two subsets: 80% for training and 20% for testing. The training set was used to train the model, enabling it to learn from the data, while the test set was kept completely separate to evaluate the model's performance on unseen data. To ensure that the split reflected the distribution of different scenarios, stratified sampling was used, maintaining a similar distribution of scenarios in both the training and testing sets. The MLP model was trained and evaluated on various configurations of physical and virtual sensor data using backpropagation. The architecture consisted of multiple fully connected layers with ReLU (Rectified Linear Unit) activation functions to introduce nonlinearity. The model used the Adam optimizer, which ensures efficient computation and convergence speed. At the output layer, the model employed a softmax function for multi-class scenario classification. The model's performance was evaluated using standard classification metrics, including accuracy for each scenario. The incremental addition (interleaving) of virtual sensors required careful handling to ensure the model could leverage the enriched feature space effectively without overfitting.