## 4 Results
### Accuracy Evaluation of Different Models
#### 4.2.2 Uncertainty Analysis  
The estimation uncertainty of 15 rain gauges in each event during the testing stage was calculated, and box-line plots were generated to depict the distribution, as illustrated in Figure 6.  
Based on the evaluation results of the quantitative indicators, the _BIAS_ and _RATIO_ of most precipitation data have a narrow error distribution interval, with a value around 1. In the case of light rain, the deviation of GPM is relatively larger, and the uncertainty interval wider compared to other data. The accuracy of the ground-interpolated data is slightly higher than that of the fused data, but this advantage decreases with increasing rainfall magnitude. FS-ConvLSTM has a smaller interval width than LSTM, which denotes higher stability. Regarding _RSME_, GPM has the highest median error in different magnitudes of rainfall, while F-SVD has a lower median value than IDW but with a larger interval width. The same difference exists between FS-ConvLSTM and LSTM, with the gap increasing as the magnitude of rainfall becomes larger. In terms of _CC_, the median values of GPM and LSTM are lower, while the median value of FS-ConvLSTM is the highest. F-SVD has a higher median value than IDW. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Data** & _BIAS_ & _RSME_ & _CC_ & _RATIO_ & _POD_ & _FAR_ & **TS** & _MAR_ \\ \hline \multirow{4}{*}{Small rain} & GPM & 2.829 & 1.461 & 0.251 & 3.829 & 0.423 & 0.704 & 0.211 & 0.577 \\  & IDW & \(-\)0.053 & 0.378 & 0.483 & 0.947 & 0.088 & 0.226 & 0.085 & 0.912 \\  & F-SVD & \(-\)0.016 & 0.278 & 0.751 & 0.984 & 0.215 & 0.078 & 0.210 & 0.785 \\  & LSTM & 1.509 & 0.651 & 0.207 & 2.509 & 0.383 & 0.687 & 0.208 & 0.617 \\  & FS-ConvLSTM & 0.764 & 0.333 & 0.753 & 1.764 & 0.157 & 0.044 & 0.156 & 0.843 \\ \hline \multirow{4}{*}{Moderate rain} & GPM & 0.970 & 2.317 & 0.151 & 1.970 & 0.381 & 0.629 & 0.232 & 0.619 \\  & IDW & \(-\)0.020 & 1.469 & 0.247 & 0.980 & 0.332 & 0.470 & 0.256 & 0.668 \\  & F-SVD & \(-\)0.011 & 1.102 & 0.649 & 0.989 & 0.463 & 0.277 & 0.394 & 0.537 \\  & LSTM & 0.323 & 1.447 & 0.214 & 1.323 & 0.329 & 0.595 & 0.222 & 0.671 \\  & FS-ConvLSTM & 0.463 & 1.018 & 0.724 & 1.463 & 0.464 & 0.262 & 0.398 & 0.536 \\ \hline \multirow{4}{*}{Heavy rain} & GPM & 1.176 & 3.641 & 0.273 & 2.176 & 0.557 & 0.515 & 0.350 & 0.443 \\  & IDW & \(-\)0.021 & 1.949 & 0.409 & 0.979 & 0.544 & 0.350 & 0.420 & 0.456 \\  & F-SVD & \(-\)0.023 & 1.535 & 0.674 & 0.977 & 0.618 & 0.212 & 0.530 & 0.382 \\  & LSTM & 0.260 & 1.983 & 0.306 & 1.260 & 0.517 & 0.485 & 0.348 & 0.483 \\  & FS-ConvLSTM & 0.263 & 1.456 & 0.718 & 1.263 & 0.634 & 0.208 & 0.543 & 0.366 \\ \hline \multirow{4}{*}{Torrential rain} & GPM & 1.063 & 4.903 & 0.314 & 2.063 & 0.594 & 0.475 & 0.386 & 0.406 \\  & IDW & \(-\)0.024 & 2.477 & 0.485 & 0.976 & 0.623 & 0.334 & 0.475 & 0.377 \\  & F-SVD & \(-\)0.027 & 1.846 & 0.748 & 0.973 & 0.708 & 0.209 & 0.602 & 0.292 \\  & LSTM & \(-\)0.161 & 2.594 & 0.364 & 0.839 & 0.571 & 0.445 & 0.391 & 0.429 \\  & FS-ConvLSTM & 0.153 & 1.753 & 0.778 & 1.153 & 0.722 & 0.197 & 0.608 & 0.278 \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of the precipitation data in different types of rainfall events. Among the classification indicators, the values of \(POD\) and \(TS\) increase, and the values of \(FAR\) and \(MAR\) decrease as the rainfall magnitude increases. In light rainfall events, \(POD\), \(FAR\), and \(MAR\) exhibit large uncertainties. For ground-interpolated data, F-SVD has a better median and interval width of indicators than IDW. For the fused data, the median indicator of FS-ConvLSTM is generally better than LSTM, except in the light rainfall events where the median values of \(POD\), \(TS\), and \(MAR\) are worse for FS-ConvLSTM. As the rainfall magnitude changes from moderate to heavy rainfall, the difference between FS-ConvLSTM and LSTM in \(POD\) and \(TS\) increases while the difference in \(FAR\) decreases. FS-ConvLSTM also has a shorter interval width, indicating higher stability.