## 2 Materials and Methods
### Generation of 4-Channel Input Factors Involving Spatiotemporal and Environmental Factors  
The study utilized Level 3 sea surface temperature (SST) data from 2016 to 2021, obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) developed by the National Aeronautics and Space Administration (NASA) (URL: [[https://oceandata.sci.gsfc.nasa.gov/l3/](https://oceandata.sci.gsfc.nasa.gov/l3/)]([https://oceandata.sci.gsfc.nasa.gov/l3/](https://oceandata.sci.gsfc.nasa.gov/l3/)) (accessed on 5 May 2024), with a spatial resolution of 9 km and a temporal resolution of 8 days. The SST values are provided in a mapped file format of netCDF.  
The mapped SST data underwent several processing steps, such as localization, interpolation, data enhancement, and cropping; subsequently, in line with reference [16], a 4-channel dataset was constructed by integrating longitude, latitude, and time. Sub  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario Name** & **Dataset Count** & **Period of Period** & **Dataset Division Principle** \\ \hline Scenario 1 & 1 & January–December & No division \\ \hline \multirow{3}{*}{Scenario 2} & \multirow{3}{*}{2} & January–May & Due to the influence of the summer monsoon, the production data from June to August are extremely limited [4]. The dataset was bisected by the monsoon boundary. \\ \cline{3-4}  & & September–December & \\ \hline \multirow{3}{*}{Scenario 3} & \multirow{3}{*}{3} & February–May & According to the results of the cluster analysis of the gravity centers of catches in reference [4], the dataset was divided into 3 parts. \\ \cline{3-4}  & & September–November & \\ \cline{3-4}  & & December–January next year & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The division of the training datasets under various scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multirow{3}{*}{**January–May**} & \begin{tabular}{c} **September–** \\ **December** \\ \end{tabular} & \begin{tabular}{c} **February–** \\ **May** \\ \end{tabular} & \begin{tabular}{c} **September–** \\ **November** \\ \end{tabular} & \
\begin{tabular}{c} **December–** \\ **Januart Next Year** \\ \end{tabular} &
\begin{tabular}{c} **January–** \\ **December** \\ \end{tabular} \\ \hline AlexNet & A & C & E & G & I & K \\ AlexNetMini & B & D & F & H & J & L \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental group codes (from A to L) associated with the cross-combination between models and datasets.  
sequently, each of these channels was converted and unified into an n by n matrix, as illustrated in Figure 2.  
The missing values in the SST matrix were interpolated by Euclidean distance interpolation with Equation (2):  
\[\hat{Z}(S_{0})=\sum_{k=1}^{N}\lambda_{k}Z(S_{k}) \tag{2}\]  
where \(\hat{Z}(S_{0})\) is the predicted value of SST at (\(S_{0}\)), \(\lambda_{k}\) is the weight of the \(k\)-th neighboring sampling point used in the prediction, and \(Z(S_{k})\) is the attribute value of the \(k\)-th adjacent sampling point.  
The formula utilized to calculate the weight \(\lambda_{k}\) is as follows:  
\[\lambda_{k}=d_{k}^{-p}\sum_{m=1}^{N}d_{n}^{-p} \tag{3}\]  
where \(d_{k}^{-p}\) is the distance between the predicted point and the \(k\)-th neighboring point, where \(p\) is the exponent, usually taken as 2.0.