## 4 Experiment and Analysis
### Reliability Test  
To discuss the effects of the aforementioned algorithms in water body extraction from remote sensing images in different periods, a remote sensing image captured on 4 October 2019 in the same region is selected. Based on this, the water bodies are classified using the  
Figure 3: Statistics of the AUC index of each algorithm applied in the three regions. same algorithms and parameters. The aim is to verify whether the experimental results of each algorithm under different image conditions are reliable and decide whether the models are universal. The model established by the data of 2019/10/20 is used in the data of 2019/10/04 for water body extraction. The statistical results of the AUC indicators of each algorithm are shown in Figure 4 (for more details, see Tables A5-A8 in Appendix A). As shown in Table 4, the AUC indicators of each machine learning algorithm for three regions all show a significant decline, with a decreased range of 0.33-66.52% As shown in Figure 4, the differences among the different algorithm performances in the three areas are obvious. In the surface complex Area2, the AUC index of the machine learning algorithms is near 0.5, which means it is difficult to extract water bodies accurately. In Areal with a simple surface environment, although the accuracy of all machine learning algorithms decreases, the errors are still within an acceptable range. In general, the decision tree algorithm has better transfer performance, with an average AUC of 0.668, and the AUC indexes of the three regions are 0.790, 0.518, and 0.697 respectively. The XGBoost algorithm has an average AUC of 0.631, and its AUC index in the three regions is 0.718, 0.512, and 0.665, respectively. The logistic regression algorithm has the worst accuracy, with an average AUC of 0.392, the AUC index in the three regions is 0.329, 0.489, and 0.357, respectively, which is inconsistent with the conclusion in Sections 4.2 and 4.3. When the model is directly transferred to remote sensing images of different periods for water extraction, the generalization ability of each machine learning algorithm is different. Among the threshold classification methods, the Otsu threshold algorithm is optimal, and its average AUC is 0.832. The AUC indexes in the three regions are 0.970, 0.617, and 0.908, respectively, which exceed the accuracy of the other machine learning algorithms. For the other two threshold algorithm, custom threshold, whose average AUC is 0.700, and the AUC indexes in the three regions are 0.842, 0.549, and 0.708 respectively. The adaptive threshold algorithm has an average AUC of 0.611, and its AUC indicators in the three regions are 0.703, 0.506, and 0.623 respectively. All in all, for different periods of remote sensing images, the threshold method is better than most of the machine learning algorithms, because the sensor imaging is affected by clouds, sun angles, and sensors. Due to the influence of the angle and other factors, the characteristics of remote sensing images will be very different during the adjacent imaging time. Even if there is no major change in the surface features, the pixel value of the remote sensing image could also change significantly. Therefore, the machine learning models trained on the data of 2019/10/20 may not be suitable for different periods.