Coastal Guard: Enhancing Maritime Border Security Through Real Time Surveillance and Predictive Alerts

Abstract

Maritime security is of paramount importance for coastal nations, given the numerous challenges posed by illegal border crossings, smuggling activities, and environmental hazards. Traditional surveillance methods have limitations in effectively monitoring vast maritime borders in real time and predicting potential security breaches. Additionally, fishermen operating in these areas are often exposed to risks such as adverse weather conditions and maritime hazards due to limited access to timely information. Hence, there is a critical need for innovative technological solutions to enhance border surveillance, communication, and coordination among maritime authorities and stakeholders. In response to these challenges, the aim of the project is to develop a solution to address the shortcomings of traditional border surveillance systems This project introduces an integrated border alert system that combines weather monitoring, hazard alerts, and seamless integration with maritime authorities. Leveraging Long Short-Term Memory (LSTM) neural networks, the FBAS aims to bolster surveillance and response capabilities. The Border Net Model, is going to develop with LSTM neural networks, enables predictive border classification, while the Alert System issues timely notifications to stakeholders. The Weather Data Provider API ensures access to up-to-date meteorological information, enhancing decision-making. Additionally, features such as alert systems for adverse weather conditions and maritime hazards ensure the safety of fishermen and other maritime activities. Through its innovative features and capabilities, the proposed system aims to bridge the gaps in traditional surveillance systems and provide comprehensive protection for maritime borders.