Coastal vulnerability assessment using the machine learning tree-based algorithms modeling in the north coast of Java, Indonesia

The north coast of Java is the center of economic activity in Indonesia. This area is dynamic and sensitive to various geo-bio-physical aspects. Therefore, a vulnerability study in this area is necessary. This study proposes a machine learning tree-based algorithms modeling approach for Coastal Vulnerability Assessment (CVA) and mapping. The tree-based algorithms used are Gradient Tree Boost (GTB), Classification and Regression Trees (CART), and Random Forest (RF). The study utilized the Google Earth Engine (GEE) platform and twelve variables as input. The prediction results of each of these modeling algorithms have been compared and evaluated to determine the most optimal performance and accuracy. Reference data was obtained from the Ministry of Maritime Affairs and Fisheries of the Republic of Indonesia (KKP). Approximately 70