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% of the reference data was allocated for training, while the remaining 30% was designated for validation. The CVA assessment yielded overall accuracies of 80.22%, 77.40%, and 71.18% based on the RF, GTB, and CART algorithms, respectively. Meanwhile, the Kappa Index for these three algorithms was 0.72, 0.67, and 0.58, indicating that the models have adequately classified the data. The research outcomes are anticipated to offer insights into the potential utilization of machine learning technology for vulnerability assessment and mapping, contributing to the management of coastal environmental issues. [ABSTRACT FROM AUTHOR]