A Machine Learning Approach on Illegal Fishing Detection Using RNN for the Area of Bauang, La Union, Philippines - Dumpit Jr, Thomas L.; Monreal, Richard (International Journal of Computing Sciences Research; Vol 9 (2025): Volume 9, 2025)
Purpose —In La Union, illegal, unreported, and unregulated (IUU) fishing threatens local livelihoods and marine ecosystems. Traditional methods are not enough to monitor wide maritime areas. This study aims to apply machine learning, particularly Recurrent Neural Networks (RNN), to detect illegal fishing by analyzing fishing patterns, vessel movements, and satellite imagery. The proposed system includes a server-side geofencing feature and a mobile client for collecting GPS data. Methodology – This research uses a mixed-methods approach, combining both quantitative and qualitative techniques. The quantitative part focuses on developing and implementing an RNN model to detect illegal fishing. The qualitative aspect involves data collection and analysis to understand the challenges and opportunities related to IUU fishing in La Union. Result – Survey findings show a need for modern monitoring solutions. About 72% of respondents use vessel monitoring systems, 64% are aware of illegal fishing, and 66% carry mobile phones. Additionally, 80% expressed interest in training, and 44% of respondents are aged 18–24, indicating high potential for adopting digital surveillance tools. Conclusion – The RNN model has proven effective in identifying and monitoring illegal fishing in real time. This technology supports marine conservation and promotes the long-term sustainability of local fishing communities. Recommendation – It is recommended to implement the RNN-based monitoring system in Bauang, La Union. This solution will enhance detection and law enforcement efforts, protect marine resources, and enable quick response actions to IUU fishing. Practical Implication s – The model has the potential for broader use. It can assist agencies like BFAR is enforcing fishing regulations and improving marine conservation nationwide. Real-time data analysis will also support sustainable fishing practices and reduce the negative impact of illegal fishing.