# Data Pipeline & AI Modeling

BLOB’s data pipeline and AI models form the core of its analytics, converting raw sensor data into clear, actionable insights. This advanced processing ensures that vast amounts of data are distilled into meaningful intelligence, avoiding information overload and supporting effective decision-making for port and environmental management.

## Data Flow Architecture

BLOB uses a multi-stage data flow starting at sensor level and ending with actionable insights for operators. Sensors collect environmental data and perform basic preprocessing. This data is then processed by onboard edge computing, where sensor fusion algorithms provide contextual insights. Real-time AI inference highlights anomalies and prioritizes critical alerts for transmission, even with limited bandwidth. Full datasets are stored onboard and uploaded at docking for archival, deeper analysis, and model training—balancing real-time awareness with long-term data value.

## Model Training Methodology

BLOB’s AI models are trained using at least a year of historical port data to capture seasonal and operational variations, establishing a baseline for anomaly detection.\
\
The training combines expert-labeled anomalies with synthetic data to cover known issues and rare edge cases. To stay accurate in the changing port environment, models are retrained every two months using new data and feedback from partners, enabling continuous improvement and adaptation to evolving conditions.

## Analytical Capabilities

BLOB’s AI system delivers key analytics for port operations and environmental management. Predictive bathymetry modeling forecasts seafloor changes using sediment and current data, helping prevent navigation hazards and infrastructure risks.\
\
Anomaly detection compares real-time data to historical norms, identifying unusual patterns through a multi-layered analysis across sensors and conditions. Trend analysis reveals gradual, long-term changes that may go unnoticed day-to-day, aiding in early issue detection. Event correlation links environmental data with activities like dredging or storms, helping distinguish natural from human-induced changes for better decision-making and compliance.

## Data Visualization and Reporting

<figure><img src="https://604958737-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2Fl3aMAxy9cbWKKAQ3d1WS%2Fuploads%2FzsV55yP6xllcK0IALQ74%2Ftech.jpg?alt=media&#x26;token=c71b9257-81c3-46f3-a192-d3cd2221abf0" alt=""><figcaption></figcaption></figure>

Though BLOB processes data autonomously, its insights are delivered through user-friendly visualization and reporting tools designed for human decision-makers. Dashboards tailor information to user roles, from executive summaries to technical details.\
\
Spatial visualizations—like 3D bathymetric maps and water quality heat maps—reveal geographic patterns, while temporal tools track changes over time for historical review and forecasting. An alert system prioritizes notifications based on severity, ensuring timely responses to critical issues.\
\
Overall, BLOB turns complex sensor data into clear, actionable intelligence that supports smarter decisions in port operations and environmental management.
