AQUA DEEPAI Powered ARGO Oceanographic Data System

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.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-03-24
DOI
https://doi.org/10.5281/zenodo.19203222
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AQUA DEEPAI Powered ARGO Oceanographic Data System

Ms. Ranjani C, Ms. Vanitha S, Ms. Sudarmani C, Ms. Shakila Banu F
Zenodo (CERN European Organization for Nuclear Research)
Oceanographic and Atmospheric Processes
article

AQUA DEEPAI Powered ARGO Oceanographic Data System

Ms. Ranjani C, Ms. Vanitha S, Ms. Sudarmani C, Ms. Shakila Banu F
article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Life below water
Openalex Percentile: Top 9%
Oceanographic and Atmospheric Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.