## 3 Results and Discussion  
The acquisition of the satellite images used in the study, pre-processing, Canny edge detection algorithm, Otsu thresholding method, machine learning methods, and evaluation metrics were all performed on the GEE platform. This research study used LAND-SAT/LC08/C02/T1_L2 Landsat 8 images available on the GEE platform. The images were taken in June, July, and August, when the cloud cover was 5% in 2014, 2017, 2020, and 2023. This collection comprises surface reflectance data that has been atmosprically corrected. The data are generated from the imagery captured by the Landsat 8 OLI/TIRS sensors. Since Landsat 8 Collection 2 data were used, a scaling factor was applied to the images. A cloud mask was implemented to enhance the accuracy of the outcomes. The Landsat 8 Collection 2 includes a quality assessment (QA) band called QA_PIXEL, which provides vital information about certain conditions seen in the data. This band enables users to implement filters on a per-pixel basis. The QA band consists of unsigned integers that indicate combinations of surface, atmospheric, and sensor conditions, which are bit-packed. The generated images underwent a median filter. The median filter decreases the image dataset by computing the median value of all the values at each pixel in the set of corresponding bands. These images were then composited to obtain a single image, as seen in Figure 2. Image composition is the process of merging images that overlap in space into a single image, where the median value for each band in the monthly collection is used. The water index images for water surface area maps were compared point-by-point with the data taken from the study region-based Sentinel image segment to show how accurate the results were. NDWI, MNDWI, and AWEI water indices were calculated from these composite images, as seen in Figure 3. All combinations, including NDWI, MNDWI, and AWEI or all  
Figure 2: Composite images from 2014, 2017, 2020, and 2023.  
three, support previous studies [77; 78] on the ability of these indices to accurately separate water and non-water pixels. The Canny operator is a highly used algorithm for image edge detection, mostly attributed to its outstanding results [79; 80]. The Canny edge detection was executed using the \"ee.Algorithms. CannyEdgeDetector\" module in the Google Earth Engine (GEE) framework. Figure 4 illustrates a specific outcome for each Canny Edge detection technique step. Figure 3: AWEI, NDWI, and MNDWI water index maps in 2014, 2017, 2020 and 2023. In this study [29], since the independent variables cannot produce reliable results when the Otsu algorithm is used alone, water and non-water places were obtained with very high accuracy using the Canny algorithm. This study presents a method that combines the Canny edge detection technique with Otsu thresholding to accurately determine the surface boundaries of lakes. According to [58], the Otsu algorithm enhances edge detection performance by optimizing Canny's dual threshold. The initial threshold of 0.7 was determined using this study's trial and error approach, which involved evaluating several average training samples in GEE. The primary purpose of the initial threshold was to distinguish between water and non-water regions and obtain preliminary water samples. The threshold values calculated for each water index were determined, as shown in Table 1. The optimum threshold values for AWEI were calculated as \(-\)0.7821, \(-\)0.7207, \(-\)0.7204, and \(-\)0.8446 for the years 2014, 2017, 2020 and 2023, in the order mentioned. The optimum threshold values for NDWI were calculated as 0.2029, 0.1412, 0.3170, and 0.7809 for the years 2014, 2017, 2020, and 2023, respectively. The optimum threshold values for MNDWI were calculated as 0.0548, 0.0234, 0.0858, and 0.0542 for the years 2014, 2017, 2020, and 2023, respectively. The results can be compared with the advantage that threshold determination is faster and simpler, especially in Lake Van, which has a larger water surface.