AI-Driven Ocean Monitoring: Real-Time Detection of Coral Bleaching, Algal Blooms, and Marine Ecosystem Shifts Using Integrated Data and Predictive Analytics

Abstract—The livelihood of human groups who depend on these resources as well as marine biodiversity are seriously threat- ened by the ocean ecosystems’ increasing degradation. The goal of this project is to create an artificial intelligence (AI)-driven ocean monitoring system that can analyze environmental data from sensor networks, underwater drones, and satellite photography to identify important events including fish population shifts, toxic algal blooms, and coral bleaching. TensorFlow, PyTorch, Apache Spark, and Dask are used for sophisticated AI-driven analytics, while Apache Kafka, Apache NiFi, and PostgreSQL are used for data streaming, ingestion, and storage in real-time data integration. In order to detect anomalous environmental events, neural networks are used in conjunction with time- series forecasting and anomaly detection algorithms for pattern identification and predictive modeling. Additionally, the system has a real-time monitoring component that generates alerts and detects anomalies using Grafana and the ELK stack. Performance studies show that the model reliably detects environmental anomalies while limiting false positives, achieving an accuracy of 88 percent, with a precision of 0.84 and recall of 0.85. By enabling prompt intervention to alleviate environmental damage and offering real-time insights into ocean health, this strategy offers a substantial advancement in marine conservation.

Index Terms—Ocean monitoring, AI-driven analytics, Coral bleaching detection, Harmful algal blooms, Fish population shifts, Neural networks, Time-series forecasting, Anomaly detection, Real-time monitoring, Environmental conservation, Satellite im- agery analysis, Underwater drones, Sensor networks, Apache Kafka, Apache NiFi, TensorFlow, PyTorch, Apache Spark, ELK Stack, Grafana.