A Predictive Model for Marine Debris Prediction (A Comparative Case Study)

Efficient removal of marine debris is imperative for safeguarding public health and the environment, particularly marine ecosystems. This research addresses the limitations of conventional methods, such as image detection and satellite data, which often exhibit an accuracy below 85% in estimating waste levels in specific geographic areas. Our primary objective is to enhance predictive accuracy by leveraging machine learning and deep learning algorithms that capitalize on historical waste generation patterns. Through an exhaustive exploration of various algorithms documented in research papers, we aim to identify the most suitable model, whether multimodal or hybrid, to improve accuracy. Performance assessment will be conducted using key metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This comprehensive approach holds the potential for significant advancements in marine debris management and environmental protection.