## 4 Results  
### Analysis of Algorithm Evaluation Results  
We have chosen the Yangbi earthquake as a study earthquake example. Different training and test data were selected to predict and calculate the error on the grid data within  
Figure 6: Anomaly extraction method flowchart. The flowchart was modified based on the anomaly extraction method proposed by [PERSON] et al. [23] and [PERSON] et al. [50] in their articles.  
Figure 7: Prediction and anomaly performance of the 31.5\({}^{\circ}\) N 102.5\({}^{\circ}\) E grid OLR values of the Luding earthquake. The red curve is the predicted OLR value, and the blue and light blue curves are the upper and lower bounds of the confidence interval, respectively. The green curve is the actual OLR value; AOLR indicates how much the true value exceeds the prediction interval.  
\(5^{\circ}\times 5^{\circ}\) for this earthquake example. For the evaluation of the performance of the different algorithms, the data need to be divided into training data and test data. Based on previous conclusions, the time of occurrence of pre-seismic thermal anomalies is irregular. In general, they occur within six months prior to the earthquake. We aimed to study close-proximity anomalies, so we chose the 30 days before the earthquake as the prediction days. Due to the lag in data acquisition and our desire to obtain the long time series distribution of the anomalies for future practical applications, our test data were sourced from 5, 10, 15, 20, 25, and 30 days before the earthquake. The OLR data are seasonal. A training period of one year was found to work best when [PERSON] et al. [23] investigated the predictive performance of OLR based on different algorithms. Data from the year before the earthquake can reduce the disturbance of seismic anomalies to some extent. Therefore, we chose the training data in this experiment to be one year (360 days) ahead of the test data. Taking the Yangbi earthquake on 21 May 2021, for example, 5-day training data: 22 May 2020-16 May 2021, test data: 17-21 May 2021, 10-day training data: 17-11 May 2021, test data: 12-21 May 2021, and so on. The study area of the algorithm is shown in Figure 1. As the original data provided data located at a \(1^{\circ}\times 1^{\circ}\) centroid, the location was recorded in the form of \(25.5^{\circ}\) N \(99.5^{\circ}\) E. The root mean square error is a common error test and has been widely used in earthquake prediction research [13; 14; 15; 19; 51]. The experiment uses the root mean square error (RMSE) to assess the error between the predicted and actual values. The total error is calculated for the same number of days tested.  
Table 2 shows the error results of the BILSTM algorithm for different days and grid points. The remaining ARMA, ARIMA, SVM, and XGBoost algorithms were evaluated separately, and the total error of the different algorithms was analyzed following the same steps as described above; not all values are shown in the paper for the sake of brevity. Table 3 shows the total RMSE for all grids of the five algorithms for different forecast days.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**BILSTM** & **5 Days** & **10 Days** & **15 Days** & **20 Days** & **25 Days** & **30 Days** \\ \hline \(23.5^{\circ}\) N \(97.5^{\circ}\) E & 35.8714 &  
Table 3 shows that the total sum of root mean square errors for all algorithms was the smallest for the 5-day prediction time; the BILSTM algorithm had the smallest sum of root mean square errors for the 5-day prediction time. Therefore, BILSTM was selected as the research algorithm for this experiment. Five days was chosen as the prediction time. Five days was a short prediction time, and we used the sliding time window method [51] in this specific experiment to achieve a prediction of thirty days.