# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 3 Results
### Practical Application for Water Level Observation
Water level thresholds for normal level, alert level, warning level, and danger level were represented by green., yellow, orange, and red colors, respectively. Figure 13: Failure in water detection during the segmentation process led to an error in water level estimation. (**a**) The segmentation result overlaid with the original image. (**b**) The estimated river water level and its threshold class. From the results obtained, it is clearly observed that the model overestimated water features at the lower right side of the images (Figure 14b), especially before the application of morphological operations. This is due to the fact that there was a brownish ground area that had a similar attribute as the water feature that led to the misclassifications. Besides, the image captured on 6 January obtained more error as compared to the other images because there were water stains on the camera lens during rain that was mistakenly classified as water region. Thereby, the two factors have a negative influence on the segmentation results. To improve these results, morphological operations were applied and some unwanted regions were removed successfully as shown in Figure 14c. However, there were some parts of water that were also removed during these morphological operations. This is because, as the water region was assumed to be one connected element in the image, water features that were disconnected from the main water element were presumed to be noises. This problem might be avoided if there were no obstacles that separate all water  
Figure 14: (**a**) The original images were captured on different days (**b**) The segmentation results obtained when using DeepLabv3+ model (**c**) Results after performing morphological operations. regions present in one particular image or different morphological operations, such as morphological dilation and erosion, were adopted during the post-processing stage. On a side note, elevation values of the on-site markers were extracted from the digital elevation model (DEM) generated from LiDAR data. A geoprocessing tool in ArcGIS software was executed to extract the cell values of the DEM raster based on a set of marker points. The elevation values of the markers were then overlaid with the post-processed water region to get an idea of how water levels fluctuated during the wet condition. There were four water level thresholds used in this study, which are normal, alert, warning, and danger levels. In particular, a green color marker represents normal level, a yellow color marker indicates alert level, while orange and red markers represent warning and danger levels, respectively, as shown in Figure 15.  
River water level estimation was carried out using the framework discussed in Section 2.5.2. After evaluating the performance of the proposed framework, it was applied to the study area which does not equip with a water level sensor. The threshold values for this study area were set at 8.8 m for normal level, 9.3 m for alert level, 9.8 m for warning level, and 10.8 m for dangerous level, respectively (Figure 15). Specifically, on 3 January, the water level exceeded the danger threshold which indicates that the water level value was 10.8 m. Moreover, the danger level demonstrates that possible flooding  
Figure 15: The segmentation results were overlaid with the water level thresholds extracted from LiDAR data to give clear visualization on the rising and falling of water levels during the wet condition. The green marker represents the normal level, the yellow marker represents the alert level, the orange level indicates the warning level and the red marker indicates the danger level. may occur and evacuation action may be initiated. On the next day, the water level dropped to alert level at 9.55 m and fell again to normal level on the 5 of January, which means the water level value was estimated to be 9.05 m. Due to rainfall on 6 and 7 January, the water level rose to warning level which demonstrates that water levels were significantly increased above the normal value during these days.