ANGLEr Behavior

Research on angler behavior focuses on predicting and understanding fishing activity to improve fisheries management and enhance angler satisfaction. Current studies employ machine learning, particularly utilizing various deep learning architectures (e.g., CNNs, transformers) and regression models, to analyze diverse data sources including online platforms, environmental factors, and vessel movement patterns. This work aims to improve the accuracy and efficiency of predicting angler presence, catch rates, and even potentially identifying illegal activities like forced labor, ultimately contributing to more sustainable and responsible fishing practices.