## 4 Conclusions  
In this study, the water surface boundaries of Lake Van, the largest lake in Turkey, were determined using the Canny edge detection algorithm, Otsu threshold determination methods, and various water extraction indices, and the performances of these metrics were evaluated and compared. Optical images collected by the Landsat satellite between 1 June and 1 September in 2014, 2017, 2020, and 2023 were used. Additionally, different algorithms  
Figure 6: Water surface boundaries of Lake Van obtained according to different water indices and machine learning algorithms.  
and indices were employed to evaluate the performance of Landsat 8 data for classification. Utilizing indices to extract water surfaces is a feasible approach compared to alternative methods [28]. Estimating the threshold for extracting water surfaces using indices is a time-consuming process, and the choice of threshold value may include a subjective judgment [83]. The absence of a standardized threshold value for water content in various water bodies across different geographical areas is the reason for this [84]. To eliminate this problem, the Canny edge detection algorithm and Otsu threshold matching methods were used. CART, RF, and SVM methods were used in classification. The classification accuracy was assessed using the metrics of UA, PA, and OA, Kappa, and F score statistical tests. It is seen that the algorithm and water extraction indices selected by the Canny algorithm and the Otsu thresholding method increase the reliability of the study. The suitable water surface detection index that has been established for Lake Van is AWEI, and the algorithm is SVM. The subsequent investigations in the literature validate these conclusions. The SVM algorithm is commonly regarded as the most favored classification method in numerous studies [2; 67].  
In order to develop a more significant correlation between the surface water in the area and the generated water index values, it is necessary to consider the unique spectral and environmental characteristics of the water. Variations in atmospheric conditions, changes in the angle of incidence of sunlight on the surface, and modifications in the chemical and biophysical properties of water can influence the particular wavelengths of light that are reflected by bodies of water [85]. Moreover, identifying water surfaces might serve as a crucial input in numerous research endeavors. Hence, the investigation into the efficacy of various data kinds, indices, and algorithms for identifying water surfaces will persist in garnering the interest of researchers. Water surface mapping is crucial for the effective management of water resources and the successful execution of engineering projects. Although cloud platforms such as GEE have made significant developments in machine learning and artificial intelligence techniques, there are still constraints in effectively detecting water bodies [14; 86]. One of the limitations we encountered in our study was that the study area was too large and therefore we could not work with Sentinel 2 images. The GEE platform has a memory capacity problem. In later studies, the aim is to make longer-term and 3-monthly observations of the lake surface. In addition to these parameters, lake surface temperature values and meteorological parameters will be included in the study, and how the lake area is affected by these parameters will be investigated. At the same time, in future studies, the boundaries of the lake area will be determined using CNN based detection (i.e., binary classification) methods.  
This research received no external funding.  
GEE freely gave all the data utilized in this study. Using this platform, anyone has access to these data.  
Thank you to the GEE platform, where data are obtained and processed free of charge.  
The author declares no conflicts of interest.