Page 251 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

sensors will improve spatial and temporal granularity of data. These platforms can fill gaps
in satellite and buoy coverage, particularly in remote or under-monitored marine regions.
• Real-Time Intervention Feedback Mechanism: Developing a two-way feedback interface
where field-level marine workers and local agencies can directly report outcomes and
anomalies back into the AI system will enhance model responsiveness and regional Change 4.2-Climate
specificity.
• Multilingual and Inclusive Alert System: Expanding the system to support multilingual
alerts and low-tech communication channels (e.g., SMS) will ensure that small-scale
fisherfolk and community stakeholders are informed and can act quickly.
• Model Explainability and Transparency: Building tools for explainable AI (XAI) to help
users and decision-makers understand why certain regions are flagged as high risk will
increase trust, adoption, and model auditing capacity..

3 Use Case Requirements

The successful deployment of the AI-Driven Early Warning System for Oceanic Dead Zones
relies on a combination of technical infrastructure, environmental data streams, institutional
partnerships, scalable Application Programming Interfaces(APIs), and responsible policy
frameworks. The following essential requirements are identified:

REQ-01: Real-Time Environmental Data Acquisition

Real-time satellite imagery (sea surface temperature, ocean colour, chlorophyll levels) and in-
situ oceanographic sensor feeds (dissolved oxygen, salinity, pH, nutrient levels) are required
from organizations such as NASA, NOAA, ESA, NIOT, and ESSO-NIOT–NCOIS. Additional data
will be collected using AUVs like SeaGliders, underwater drones, CTD profilers, MODIS Aqua
satellite feeds and Argo floats to enhance spatial and temporal resolution.

REQ-02: High-Performance Computational Infrastructure

Cloud or on-premises systems equipped with Graphics Processing Units(GPUs) or Tensor
Processing Units(TPUs) are required to facilitate training and inference of deep learning models
on large-scale, multi-source ocean datasets. The system architecture must support scaling for
high-volume geospatial data.

REQ-03: Multi-Source Data Integration & Preprocessing Pipeline
The system must support ingestion and integration of heterogeneous data formats, including
Network Common Data Form(netCDF)(satellite data)), Comma-Separated Values(CSV) (sonar
data)), and real-time APIs (NASA EarthData, NOAA NCEI File Transfer Protocol(FTP), CMEMS
RESTful services). Automated pipelines will normalize, align, and fuse datasets dynamically.

REQ-04: AI Model Development, Retraining, and Continuous Learning

Deep learning pipelines incorporating CNNs, LSTMs, and ensemble architectures must be
modular and support continual retraining based on incoming new environmental data and
real-world validation feedback. Models should adapt to regional oceanographic changes to
maintain predictive accuracy.

REQ-05: Decision-Support Dashboard and Stakeholder Alert System

A dynamic geospatial dashboard will visualize hypoxia risk forecasts. Alerts must be
disseminated to stakeholders—marine agencies, fisheries departments, and coastal governance

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