## 2 Data Collection
### Convolutional Gated Recurrent Unit (ConvGRU)  
The ConvGRUs represent an evolution in neural network architecture, demonstrating enhanced learning capabilities over conventional GRU networks. These networks are adept not only at handling temporal data sequences but also at capturing and integrating the spatial characteristics of two-dimensional input data, akin to the functionality of CNNs. This dual capability makes ConvGRU networks more advanced in terms of learning efficiency, compared to traditional GRU networks. One of the distinctive features of ConvGRU networks is their departure from the nearly fully connected framework typical of LSTM and GRU networks. The traditional approach tends to accumulate excessive information, leading to inefficiencies, and often fails to account for spatial correlations among adjacent pixels in two-dimensional datasets. ConvGRU networks address this limitation by incorporating a convolutional structure, replacing the dot-product operations commonly found in GRU networks with convolutional processes. This shift enables a more nuanced handling of spatial data [53]. In ConvGRU networks, both inputs and outputs are conceptualized as three-dimensional tensors, encapsulating a rich array of spatial features within the time-series data. Building upon the inherent memory capabilities of GRU networks, ConvGRUs are proficient at maintaining the spatial dynamics of input-image sequences over extended periods. This ability facilitates the effective transmission of feature information across prolonged temporal spans, significantly enhancing the accuracy and reliability of spatiotemporal predictions. The main equations of ConvGRU used in this research are as follows [37]:  
\[z_{t}=\sigma(W_{xz}*X_{t}+W_{hz}*h_{t-1}+b_{z}) \tag{12}\]  
\[r_{t}=\sigma(W_{xr}*X_{t}+W_{hr}*h_{t-1}+b_{r}) \tag{13}\]  
\[\hat{h}_{t}=f(\,W_{xh}*X_{t}+r_{t}\circ(W_{hh}*h_{t-1})+b_{h}) \tag{14}\]  
\[h_{t}=(1-z_{t})\circ\hat{h}_{t}+z_{t}\circ h_{t-1} \tag{15}\]  
\[X_{t}\in\mathbb{R}^{C_{i}\times H\times W} \tag{16}\]  
\[X_{t}\in\mathbb{R}^{C_{i}\times H\times W} \tag{17}\]  
The ConvGRU equations exhibit a structural resemblance to the GRU, with a notable modification in their computational approach. The pivotal change in ConvGRUs lies in the substitution of dot-product operations, commonly used in GRUs, with convolution operations. This alteration fundamentally enhances the network's capacity to process spatial data effectively. In ConvGRUs, the ReLU function is employed as the activation function. This choice is instrumental in introducing non-linearity into the network, enabling it to effectively handle complex input patterns. Each new input to the ConvGRU, presented in the form of a tensor, triggers the reset gate. This gate assesses whether to incorporate the network's previous state in processing the new input. Simultaneously, the update gate determines the degree to which the new input data should influence the current state \(h_{t}\) of the network. The input structure of ConvGRU networks is characterized by tensor dimensions. Here, \(H\) and \(W\) denote the height and width of the tensors, respectively, encapsulating the spatial dimensions of the input. Meanwhile, \(C_{h}\) and \(C_{i}\) represent the channel sizes of the state and input tensors, respectively. These dimensions are critical in defining the network's capacity to process varying sizes and complexities of input data, as detailed in equations 18 and 19. The adoption of ConvGRU networks, as indicated by [PERSON] et al. [37], underscores the progressive shift towards integrating convolutional processes in RNN architectures. This integration significantly broadens the scope and efficacy of neural networks in handling sophisticated spatiotemporal data sets. A critical aspect of this model is the dynamic adaptation of the loss function throughout the training phase. This continual adaptation ensures a progressive decrease in the cost-function value, thereby enhancing the efficiency and accuracy of the training process.