# Deep Learning Semantic Segmentation for Water Level Estimation Using Surveillance Camera
## 5 Conclusions  
This work presented state-of-the-art semantic segmentation methods using two different networks, namely DeepLabv3+ and SegNet, for detecting water bodies before estimating water levels and observing water fluctuation from surveillance images. DeepLabv3+ was created with ResNet as the backbone, while SegNet used VGG-16, and a comparison between these two approaches has been done in this work. Both networks could distinguish features of the water from the background with some misidentification of pixels due to similar features. This work focused on the accuracy factor only without considering the training time and memory usage. When measuring semantic segmentation metrics on the overall test set results, it was found that the DeepLabv3+ network performed better than the SegNet network by achieving above 93% for overall accuracy and IoU metrics, and approximately 82% for BF score while SegNet achieving averages of 91% and 67.20% respectively. For individual class performance, DeepLabv3+ still outperformed SegNet by having higher overall accuracy, IoU, and BF score. When predicting new images using both trained networks, the results show that both networks successfully distinguished water regions from the background. However, the outputs from DeepLabv3+ were more accurate than the results from the SegNet network. Therefore, DeepLabv3+ was used in the segmentation process for a set of images collected for five straight days to estimate river water levels and determine the rising and falling (fluctuation) of water levels in the study area. When both segmentation results and water level markers were overlaid, the fluctuation of water levels can be clearly visualized and water level threshold can be obtained, hence it is assumed that it could help the authorities to make a clear judgment in planning and decision making. Water level estimation was predicted based on the elevations from the pre-defined markers. The values were estimated either from the exact elevation value of the marker or by computing the average values between the two markers. To investigate the performance of the proposed method, the relationship between the estimated water level and water level measured by the sensor was evaluated by using [PERSON]'s rank-order correlation coefficient. The correlation obtained was 0.91 which indicates a strong relationship between the water level measured by the sensor and water level estimated from the proposed framework. Based on these findings, it has been concluded that the proposed approach is a promising technique that can be employed to extract water regions as well as estimate water levels from surveillance images for flood studies. By identifying the flooded regions, it is believed that the information might help the authorities to focus on the most vulnerable regions during flood events. Moreover, by combining the segmentation results with other data such as LiDAR data, it is expected that the outputs could provide additional information for flood management and related activities. The proposed study has the potential as an alternative monitoring system that offers information such as precise locations and conditions of the affected area at a low cost. Furthermore, if the telemetry system failed to provide data due to severe flooding, it can act as a substitute solution because the proposed work could also provide the level of severity of the flood as well as approximate water level. Future work could be done by having an extensive labeled dataset and hyperparameter fine-tuning to improve the segmentation accuracy. In addition, the same work could also be explored with a nighttime dataset to determine how well the neural network functions when using images that are captured at night. Furthermore, an extensive study on extracting more information from flooding images, such as calculating the flood depth and flooded area, is ongoing, which it is believed can be done in a GIS environment. N.A.M. : conceptualization, formal analysis, methodology, writing--original draft, visualization. A.F.A.