Evaluating Four Machine Learning Algorithms Using Google Earth Engine for Analyzing LULC Dynamics of Sandwip Island, Bangladesh

Sandwip Island is a highly dynamic coastal environment experiencing continuous geomorphological changes driven by erosion, accretion, and sediment deposition. This study analyzes shoreline changes from 1995 to 2026 using the Digital Shoreline Analysis System (DSAS) in ArcGIS and evaluates Land Use Land Cover (LULC) changes from 2000 to 2024 using Google Earth Engine and machine learning algorithms, including Classification and Regression Tree (CART), Gradient Boosting Trees (GBT), Random Forest (RF), and Support Vector Machine (SVM). A total of 140 ground-truth points representing seven LULC classes were used to assess model accuracy. Among the models, RF demonstrated the highest overall accuracy (93.57%), producing clearer boundaries and better separation of major land-cover classes. GBT achieved an accuracy of 92.14%, showing reliable classification across most LULC types. SVM reached a moderate accuracy of 90%, although some overgeneralization was observed in spectrally similar classes. CART showed the lowest accuracy (87.14%), with increased mixed-pixel occurrence in dense and heterogeneous regions. The findings reveal a gradual increase in the total area of Sandwip Island due to sediment deposition, contributing to the expansion of sandy areas (61.628 km²), mangroves (27.412 km²), vegetation (58.618 km²), cropland (31.587 km²), and built-up areas (60.031 km²). These results highlight the significant influence of natural coastal processes on land-cover dynamics and demonstrate the effectiveness of remote sensing and machine learning techniques for monitoring dynamic coastal environments. The study further suggests that integrating sediment transport modeling and tidal-current analysis with machine learning approaches could improve the accuracy of future coastal change assessments.