Forecasting future scenarios of coastline changes in Türkiye's Seyhan Basin: a comparative analysis of statistical methods and Kalman Filtering (2033–2043)

Complex changes in coastlines are increasing with climate, sea level, and human impacts. Remote Sensing (RS) and Geographic Information Systems (GIS) provide critical information to rapidly and precisely monitor environmental changes in coastal areas and to understand and respond to environmental, economic, and social impacts. This study aimed to determine the temporal changes in the coastline of the Seyhan Basin, Türkiye, using Landsat satellite images from 1985 to 2023 on the Google Earth Engine (GEE) platform. The approximately 50 km of coastline was divided into three regions and analyzed using various statistical techniques with the Digital Shoreline Analysis System (DSAS) tool. In Zone 1, the maximum coastal accretion was 1382.39 m (Net Shoreline Movement, NSM) and 1430.63 m (Shoreline Change Envelope, SCE), while the maximum retreat was -76.43 m (NSM). Zone 2 showed low retreat and accretion rates, with maximum retreat at -2.39 m/year (End Point Rate, EPR) and -2.45 m/year (Linear Regression Rate, LRR), and maximum accretion at 0.99 m/year (EPR) and 0.89 m/year (LRR). Significant changes were observed at the mouth of the Seyhan delta in Zone 3. According to the NSM method, the maximum accretion was 1337.72 m, and maximum retreat was 1301.4 m; the SCE method showed a maximum retreat of 1453.65 m. EPR and LRR methods also indicated high retreat and accretion rates. Statistical differences between the methods were assessed using the Kruskal–Wallis H test and ANOVA test. Generally, NSM and EPR methods provided similar results, while other methods varied by region. Additionally, the Kalman filtering model was used to predict the coastline for 2033 and 2043, identifying areas vulnerable to future changes. Comparisons were made to determine the performance of Kalman filtering. In the 10-year and 20-year future forecasts for determining the coastline for the years 2033 and 2043 with the Kalman filtering model, it was determined that the excessive prediction time negatively affected the performance in determining the coastal boundary changes.