Cloud-Integrated Bayesian Methods for Marine Sediment Contamination Detection and Environmental Protection

Contamination of marine sediments presents significant threats to aquatic ecosystems and human health, requiring precise and effective detection techniques. Traditional methods for evaluating sediment quality are often laborious and time-consuming, making them unsuitable for real-time environmental monitoring. This research presents a cloud-integrated methodology using Bayesian techniques to identify marine sediment pollution. Due to their capacity to manage uncertainty and partial data, Bayesian approaches provide a probabilistic framework for analyzing complex environmental data. When integrated with cloud computing, these techniques provide real-time processing and analysis of extensive sensor data from maritime settings. Cloud integration enables scalable, adaptable, and accessible solutions for continuous monitoring, facilitating the prompt identification of pollution and supporting informed decision-making for environmental protection. The proposed system utilizes Bayesian models to forecast contamination levels and assess the risks associated with various environmental conditions, thereby enhancing the precision and reliability of contamination detection. This method provides an effective tool for enhancing marine environmental monitoring, facilitating early detection of threats, and supporting the sustainable management and conservation of marine ecosystems.