Model-level Feature Fusion
To further enhance the model’s performance, this study employs a CNN network and a Bi-GRU network to extract spatial and temporal features from the input dataset, respectively. On this basis, an Add network is used to fuse feature tensors from different network layers element-wise, constructing a high-dimensional spatiotemporal feature matrix. This enables the model to learn deeper and more comprehensive feature information.
1)CNN architecture: Convolutional Neural Networks (CNNs) are renowned for their advantages in spatial information processing, and the structure of the CNN model is illustrated in Figure 2. By leveraging local perception and weight sharing, CNN models can map input data into high-dimensional space and efficiently extract key features from the input data [34]. In the structure of the CNN model, the convolutional layer plays a crucial role, using convolutional kernels with shared weights to extract features from the input data and applying activation functions to perform nonlinear mapping of neurons, thereby capturing complex relationships within the data. The pooling layer then reduces the dimensionality of the features obtained from the convolution operation through methods such as max pooling or average pooling, thereby compressing the feature quantity and reducing the risk of overfitting.
In the context of time series data, CNN models also demonstrate powerful feature extraction capabilities [35]. Although CNNs were initially designed for image data processing, their applicability to time series data stems from their ability to effectively extract spatial features. Time series data can be viewed as spatial data points arranged sequentially along the time axis, allowing CNNs to utilize their local perception and weight sharing properties to discover and exploit spatial correlations among data points within the time series. This capability enables CNNs to capture important features in time series data, thereby enhancing the model’s performance and generalization ability.