Haoyu Li¹ and Marisabel Chang², ¹USA, ²California State Polytechnic University, USA

The EcoFishCast system is an innovative tool designed to predict Dissolved Inorganic Carbon (DIC) levels in oceanographic environments using machine learning models [1]. By integrating a mobile application with a robust backend server, the system allows users to input environmental data and receive accurate predictions. Experiments conducted as part of the project identified Gradient Boosting and Random Forest as the most reliable models, particularly when combined with data scaling techniques, which significantly improved prediction accuracy [2][3]. While the system performs well, future enhancements are planned to address limitations related to training data diversity and computational efficiency, ensuring EcoFishCast remains a powerful and reliable resource for oceanographic analysis.

Dissolved Inorganic Carbon (DIC), Marine Science, Mobile Application, Machine Learning, Oceanographic Analysis