## 5 Results and Discussion  
This study designed a pipeline leak detection method by combining a 1D CNN and LSTM into a united structure. Considering CNN's advantage in reducing frequency \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Output Shape** & **Parameter** \\ \hline Convolution 1D & (None, 11,998, 64) & 256 \\ Max pooling 1D & (None, 5999, 64) & 0 \\ Convolution 1D & (None, 5997, 128) & 24,704 \\ Max pooling 1D & (None, 2998, 128) & 0 \\ LSTM & (None, 2998, 100) & 91,600 \\ LSTM & (None, 50) & 30,200 \\ Dropout & (None, 50) & 0 \\ Dense & (None, 2) & 102 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hybrid CNN-LSTM network architecture summary. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Values** \\ \hline Convolution 1D layer 1 filters & 64 \\ Convolution 1D layer 2 filters & 128 \\ LSTM layer 1 units & 100 \\ LSTM layer 2 units & 50 \\ Dropout rate & 0.1 \\ Optimizer & Adam \\ Learning rate & 0.001 \\ Batch size & 64 \\ Number of epochs & 20 \\ Activation functions & ReLU (Conv1D, LSTM), Sigmoid (output) \\ Loss function & Categorical cross entropy \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter values. variance and LSTM's advantage in the temporal model, the CNN output is taken as the input of LSTM. Figure 8 shows the training and validation loss versus the epoch result. Figure 9 depicts the training and validation accuracy versus the epoch result for all four datasets of the proposed model. The model was compared with two other methods, TD-LSTM and BiGRU. The TD-LSTM model combines two different approaches to improve overall performance. It integrates an LSTM network with time domain features to obtain an advantage from both temporal patterns and domain-specific features for better prediction accuracy. The LSTM component of the model processes sequential data, such as time series, with a layer that has 64 units. This LSTM layer captures dependencies and patterns over time in the input sequences. In parallel, the time domain feature branch processes from the raw signals the static features, such as mean, standard deviation, variance, skewness, and kurtosis. These features may relate to the sequence data but are not inherently temporal. This branch uses a dense layer with 128 units and ReLU activation to transform these features into a more useful representation. By combining these two streams of information, the model can effectively learn from both the sequential patterns and the specific features of the data. After merging the outputs from the LSTM and time domain branches, the model includes a dense layer with 64 units to further process the combined features before producing the final output. The model is then trained using the combined feature pool resulting from the time domain feature and the LSTM features. The BiGRU model is a type of neural network specifically designed for sequence data, such as time series or text. The core of this model is based on bidirectional gated recurrent units, which are a variant of recurrent neural networks (RNNs). In this model, the first layer consists of a bidirectional GRU with 16 units, meaning that it processes the input sequences in both forward and backward directions. This bidirectional approach helps the model capture patterns and dependencies in the sequence more effectively, as it can utilize context from both directions. Figure 8: Training and validation loss versus epochs of the proposed method: (**a**) Gas-13 bar, (**b**) Gas-18 bar, (**c**) Water-13 bar, and (**d**) Water-18 bar. Following the initial layer, another bidirectional GRU layer with eight units is used to further refine the features extracted from the sequences. This additional layer enhances the model's ability to understand complex patterns in the data by combining information from multiple time steps. After the GRU layers, a dropout layer with a rate of 0.1 is applied to prevent overfitting, which makes the model more generalizable to unseen data.