## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline Neural Network & water & 0.982 & 0.690 & 0.810 & 0.935 \\  & other & 0.889 & 0.995 & 0.939 & 0.935 \\ Random Forest & water & 0.999 & 0.628 & 0.771 & 0.935 \\  & other & 0.870 & 1.000 & 0.930 & 0.935 \\  & water & 0.995 & 0.567 & 0.722 & 0.923 \\  & other & 0.852 & 0.999 & 0.919 & 0.923 \\  & water & 0.998 & 0.671 & 0.802 & 0.941 \\ XGBost & other & 0.883 & 0.999 & 0.938 & 0.941 \\  & water & 0.997 & 0.624 & 0.768 & 0.933 \\  & other & 0.869 & 0.999 & 0.929 & 0.933 \\  & water & 0.984 & 0.682 & 0.805 & 0.935 \\  & other & 0.886 & 0.995 & 0.938 & 0.935 \\  & water & 0.405 & 0.747 & 0.525 & 0.625 \\  & other & 0.846 & 0.558 & 0.673 & 0.625 \\  & water & 0.497 & 1.000 & 0.664 & 0.748 \\  & other & 1.000 & 0.593 & 0.745 & 0.748 \\  & water & 0.970 & 0.893 & 0.930 & 0.964 \\  & other & 0.958 & 0.989 & 0.973 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistics of various indexes of each algorithm in Area3 on October 20. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline Neural Network & water & 0.708 & 0.595 & 0.647 & 0.850 \\  & other & 0.992 & 0.995 & 0.993 & 0.850 \\ Random Forest & water & 0.962 & 0.241 & 0.385 & 0.973 \\  & other & 0.984 & 1.000 & 0.992 & 0.973 \\ SVM & water & 0.585 & 0.712 & 0.642 & 0.789 \\  & other & 0.994 & 0.989 & 0.992 & 0.789 \\ XGBost & water & 0.961 & 0.221 & 0.360 & 0.972 \\  & other & 0.984 & 1.000 & 0.992 & 0.972 \\ Logistic Regression & water & 0.869 & 0.498 & 0.633 & 0.929 \\  & other & 0.990 & 0.998 & 0.994 & 0.929 \\ Decision Tree & water & 0.948 & 0.144 & 0.250 & 0.965 \\  & other & 0.982 & 1.000 & 0.991 & 0.965 \\ Adaptive Threshold & water & 0.027 & 0.670 & 0.052 & 0.507 \\  & other & 0.986 & 0.496 & 0.660 & 0.507 \\  & water & 0.256 & 0.825 & 0.391 & 0.931 \\  & other & 0.996 & 0.950 & 0.972 & 0.626 \\  & water & 0.856 & 0.361 & 0.508 & 0.922 \\  & other & 0.987 & 0.999 & 0.993 & 0.922 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of various indexes of each algorithm in Area2 on October 20. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Category** & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline Neural Network & water & 0.379 & 0.999 & 0.550 & \\  & other & 0.996 & 0.097 & 0.177 & 0.688 \\ Random Forest & water & 0.377 & 1.000 & 0.547 & \\  & other & 1.000 & 0.087 & 0.160 & 0.688 \\ SVM & water & 0.020 & 0.001 & 0.001 & \\  & other & 0.643 & 0.994 & 0.781 & 0.331 \\  & water & 0.435 & 1.000 & 0.607 & \\ XGBost & other & 1.000 & 0.284 & 0.442 & 0.718 \\ Logistic Regression & water & 0.017 & 0.001 & 0.001 & \\  & other & 0.642 & 0.990 & 0.779 & 0.329 \\ Decision Tree & water & 0.579 & 1.000 & 0.734 & 0.790 \\  & other & 1.000 & 0.599 & 0.749 & 0.790 \\ Adaptive Threshold & water & 0.493 & 0.918 & 0.641 & 0.703 \\  & other & 0.913 & 0.478 & 0.628 & \\  & water & 0.685 & 0.998 & 0.812 & 0.842 \\ Custom Threshold & other & 0.998 & 0.746 & 0.854 & 0.842 \\  & water & 0.949 & 0.985 & 0.967 & \\  & other & 0.992 & 0.971 & 0.981 & 0.970 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of various indexes of each algorithm in Area1 on October 4.