# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 3 Results
### Practical Application for Water Level Observation  
Before applying the river water level estimation in the study area, the work concept was used in one of the DID stations, Sungai Bernam at Kampung Selisek Selangor, that equipped with both a surveillance camera and water level sensor to validate its efficiency. For this particular location, the water level thresholds for normal, alert, warning, and danger are 24 m, 26.1 m, 26.6 m, and 27.1 m, respectively. Statistically, the relationship between both estimated water level and observed water level from the sensor was evaluated using [PERSON]'s rank-order correlation coefficient. It is found that the correlation between the two variables was 0.91, which indicates that the correlation is strong. Therefore, the proposed method could be used for application in the study area with no water level sensor available. Visually, Figure 12 shows the comparison between water level estimation from the proposed work and water level obtained from the sensor. In general, the estimated water level follows the fluctuation pattern measured by the sensor. However, it can be seen that there were a few points of estimated water level that were significantly different from the one measured by the sensor, especially within the normal range. This is because the range gap between normal and alert levels is quite far, and the study has markers only at normal and alert points with no other points in between. Hence, the large errors between both measurements. Therefore, more elevation points are needed to reduce the errors between the estimated and observed water levels. Nevertheless, it is more important to focus on alert, warning, and danger levels. For the three categories, the proposed method tends to overestimate the water level at some points, but still within the proposed categories. Nonetheless, more obvious errors were found when the method underestimated the water level. It is found that errors in water segmentation results were the major factor that contributes to this problem. If the segmentation model failed to identify the water region correctly, it led to errors during water level estimation. For instance, the water region in Figure 13 was not segmented correctly, which results in incorrect water level estimation. The water level in Figure 13 should be at the danger level, but it was identified as a warning level. This problem can be avoided by preparing more training datasets for the segmentation model to improve the segmentation accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Author(s)** & **Segmentation Networks** & **IoU (\%)** \\ \hline Proposed approach & \begin{tabular}{c} DeepLabv3+ \\ SegNet \\ \end{tabular} & 94.37 \\ \hline [PERSON] et al. [70] & \
\begin{tabular}{c} DeepLab \\ UperNet \\ \end{tabular} & 93.32 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} [PERSON] et al. [29] \\ \end{tabular} } & Tiramisu & 81.91 \\ \cline{1-1}  & Pix2 pix & 72.25 \\ \cline{1-1}  & FCN-8s & 70.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A comparison between results from different segmentation networks used in this work and the previous study. Based on the findings, the DeepLabv3+ model was used to segment a set of surveillance images in the study area captured at five consecutive days. Water regions were detected before the integration with water level information from LiDAR data to see the fluctuation of water level as well as to estimate water level in the study area. After the segmentation process took place, a binary mask for the water region was created and the morphological operations were carried out to remove noises and imperfections in the segmentation outputs, as shown in Figure 14.  
Figure 12: Comparison between the estimated water level and observed water level measured by the sensor. The blue line indicates water level from the sensor while the black line represents water level estimation from the proposed method.