This research paper introduces AquaDeep, an artificial intelligence-powered ocean monitoring platform designed to analyze complex marine telemetry and predict environmental variables in real-time. Since critical indicators like temperature, salinity, and pressure are essential for monitoring climate change and ecosystem health, this study addresses the accessibility gap caused by traditional, manual processing of large NetCDF datasets. The core objective is to design and implement an automated ETL pipeline that extracts key subsurface parameters from the global ARGO network and utilizes Random Forest Regression to classify ocean states and identify anomalies. By integrating Retrieval-Augmented Generation (RAG), the system successfully converts raw scientific records into actionable natural language insights, fulfilling the goal of developing a scalable, real-world monitoring platform that democratizes access to ocean intelligence for maritime stakeholders and sustainable fisheries management.
Ms. Ranjani C, Ms. Vanitha S, Ms. Sudarmani C, Ms. Shakila Banu F (Tue,) studied this question.