# Early Earthquake Detection Using Batch Normalization Graph Convolutional Neural Network (BNGCNN)
## 4 Results
### Experimentation  
All of the experiments are carried out on an Intel Core i7-7700 processor running at 3.60 GHz with 16 GB of RAM, an NVIDIA GeForce GTX 1080 graphics card, Windows 10, and TensorFlow 2.3 with CUDA toolkit, respectively. To optimize the network parameters, we started our model parameters as the parameters of the model [18]. The experiments are designed to compare two different types of neural network models, GNN and BNGCNN, with the ultimate goal of developing a generalized detection model as a result of the findings. A random 80/20 split is performed on the entire data set to create a training set and a validation set, respectively. Training set constitutes 80 percent of the whole data with 1140 out of 1427 events. This dataset is used for training the model. Remaining 20 percent data used as a validation set to validate the model performance with 287 out of 1427 events. We evaluate the performance of the trained model on both the training and validation data sets separately as [18]. Several models can be compared to one another with the use of an independent test set to determine which is the most efficient. In addition, it should be emphasized that k-Fold cross-validation would almost probably result in somewhat better-performing models with smaller sample sizes, but this has not been confirmed in this study. Finding the optimal parameters for a model requires extensive training across a large number of epochs. The term \"one epoch\" refers to when a model passes through all of the examples once in a forward pass and once in a backward pass in a dataset or a batch of data. A large, complicated, and noisy dataset necessitates the use of more epochs by the model. To do this, early halting from evaluating validation performance is employed. A model may be trained for 30 epochs and then the best parameters are selected from the model with the highest validation performance, as an example. Positive class labels are less likely to be mislabeled in training data than negative class labels, hence models with higher accuracy on the positive class are preferred over models with higher accuracy on the negative class. Figure 5 shows the training loss and validation loss with a different number of epochs (50 to 500). A model's training loss reveals how well it fits the training data, whereas a model's validation loss tells how well it fits new data that was used for validation. Our model is showing the best performance over 400 epochs where the validation loss is minimum and the model best fits the new data. Figure 4: The histogram in (**a**) shows the magnitude distribution and the histogram in (**b**) shows the depth distribution of the events from the Southern California dataset used in experiments by the proposed model. Batch size refers to the number of instances that are sent to the model at one time for processing in a single iteration of the model. A large batch would take more memory on the GPU, resulting in slower training operations. Figure 4(b) shows the performance of our model over different batches. We put our model through its paces on 16 different batches of varying sizes (4 to 64). The results demonstrate that a batch size of 32 produces superior results by obtaining the lowest validation loss value of 0.107, which is consistent with the findings of [6]. Figure 4(c) depicts the performance of our model when different dropout values are considered. To protect the network against noise and overfitting, dropouts are used. If the model is trained on an insufficient dataset, it may encounter the problem of overfitting. An alternative method would be to increase the dataset size or decrease the number of hidden units utilized for feature computation. Dropout is a model feature that deletes or deactivates inactive units in the model's hidden layer. These units are not included in the calculations for the following rounds of the algorithm. Figure 4(c) illustrates that the model gets the maximum accuracy on a 0.8 dropout value.