Building Resilient Infrastructure with AI: The H2O.ai Flood Intelligence Blueprint, accelerated by NVIDIA

When disaster strikes, every second counts. Floods remain one of the most destructive natural disasters globally, impacting millions of lives and critical infrastructure each year. To help governments and industries act faster and smarter, H2O.ai has developed a Flood Intelligence Blueprint — a reference workflow that demonstrates how multi-agent AI systems, accelerated by NVIDIA Nemotron models, NVIDIA NeMo Agent Toolkit and H2O.ai’s Enterprise h2oGPTe platform, can deliver real-time insights for early warning, risk management, and emergency response.

This blueprint isn’t just about predicting floods — it’s about protecting mission-critical infrastructure, from dams and bridges to power grids and transport networks. Its modular architecture makes it extensible to other public-safety domains like wildfire prediction, drought analysis, and dam-failure prevention.

Architecture Overview: Multi-Agent AI for Predictive Resilience

At the core of the Flood Intelligence Blueprint lies a hybrid multi-agent architecture integrating predictive AI and generative AI workflows.

Backend Stack

• FastAPI for service orchestration
• Redis Queue (RQ) for distributed background jobs
  
• Dual AI integration between NVIDIA NeMo Agent Toolkit and H2O h2oGPTe, accelerated by Nemotron
• SQLite/PostgreSQL for data persistence
• OIDC/OAuth2 for secure authentication

Frontend Experience

• Built with React 19 + TypeScript, TailwindCSS, and shadcn/ui
  
• Real-time dashboards visualize live alerts, flood maps, and agent insights
  

Performance & Scalability

• Sub-second inference latency across hundreds of monitoring points
  
• NVIDIA NIM microservices for secure, reliable deployment of AI model inferencing, anywhere
• 99.9 % uptime for production deployments

Real-World Use Cases: From Flood Intelligence to Infrastructure Protection

The Flood Intelligence Blueprint serves as a reference workflow for how multi-agent AI can safeguard communities, infrastructure, and national assets. Using the live web app at flood-prediction.cloud-dev.h2o.dev, agencies and developers can explore how these workflows function in practice.

1. Flood Risk Forecasting and Early Warning Systems

Regional emergency management agencies can deploy the blueprint to generate real-time risk scores and seven-day flood forecasts using data from USGS, NOAA, and Open-Meteo.

• AI agents continuously evaluate rainfall, streamflow, and river-discharge trends.
  
• The dashboard prioritizes alerts by severity and geography, providing hour-by-hour risk updates.
• Field teams can act on automated evacuation and resource-allocation recommendations from the Emergency Coordinator Agent.

2. Urban and Critical Infrastructure Monitoring

Utilities and municipalities can integrate the blueprint into existing SCADA or GIS systems to monitor assets near flood plains — such as power substations, rail networks, or water-treatment facilities.

• Predictive maintenance workflows anticipate asset stress and potential failures.
• Integration with NVIDIA NeMo Agent Toolkit enables agent-to-agent coordination for multi-site monitoring, ensuring high-priority regions receive immediate attention.

3. Insurance and Risk Analytics

Insurers can use the blueprint’s agentic risk models to analyze historical and forecasted exposure, allowing proactive adjustments to policies and claims workflows.

• The Risk Analyzer Agent synthesizes climate and infrastructure data to simulate potential loss scenarios.
  
• Insights can be exported via APIs to existing underwriting or catastrophe-modeling systems.
  

4. Research and Climate Resilience Planning

Universities and national labs studying hydrology or climate adaptation can extend the blueprint to explore physics-based or multi-modal modeling.

• The open-source architecture supports experimentation with satellite imagery, drone data, and IoT sensors.
• RAG pipelines in h2oGPTe allow researchers to query flood datasets conversationally and generate structured analytical reports for policymakers.
  

5. AI for Public Safety 

For federal agencies responsible for public safety — this blueprint can power AI-assisted command dashboards that unify weather, terrain, and infrastructure data streams.

• Autonomous agents assess emerging threats and simulate cascading impacts (e.g., a dam overflow leading to power-grid disruptions).
• Sovereign AI deployment ensures compliance with FedRAMP-like and classified-network requirements, making the system viable for sensitive mission environments.

NVIDIA NeMo Agent Toolkit: Multi-Agent Intelligence in Action

The NVIDIA NeMo Agent Toolkit is an open-source agent framework that simplifies building and optimizing agentic systems. This blueprint demonstrates how NVIDIA NeMo Agent Toolkit  orchestrates specialized agents for reasoning, prediction, and coordination:

• Risk Analyzer Agent: Uses LLM-powered reasoning to compute flood-risk scores and trend correlations.
• Predictive Intelligence Agent: Performs ensemble flood forecasting (1-hour → 7-day horizons).
• Emergency Coordinator Agent: Automates alerts, evacuation routes, and resource allocation.
  

Each workflow is YAML-configured using React Agent patterns and NVIDIA Nemotron models such as Llama-3.1-Nemotron-70B-Instruct and nvidia/llama-3_3-nemotron-super-49b-v1_5.
Developers can replicate or extend these agent workflows to other scenarios — wildfire containment, dam-integrity monitoring, or coastal-erosion forecasting.

H2O.ai h2oGPTe: Sovereign Generative AI for Decision Support

H2O.ai’s Enterprise h2oGPTe, accelerated by NVIDIA Nemotron reasoning models, provides the foundation for sovereign AI deployment — essential for public-sector and critical-infrastructure environments.

• Agentic AI: Multi-agent orchestration for reasoning and automation
• Retrieval-Augmented Generation (RAG): Contextual Q&A from private flood datasets
• Document AI: Structured outputs for compliance and auditability
• Dynamic Model Routing: Balances accuracy, latency, and cost across NVIDIA and H2O models
• Streaming & Async Inference: Enables real-time dashboards and interactive monitoring

H2O.ai h2oGPTe is built to meet FedRAMP High and equivalent security standards, it enables agencies to safely adopt multi-LLM systems, including NVIDIA Nemotron models, for mission-critical applications such as disaster response and infrastructure resilience. The NVIDIA AI Factory for Government validated design reduces complexity and deployment risk through structured architectures, accelerated time-to-value, and consistent operational performance — ensuring AI systems run securely and predictably across on-premise or hybrid environments.

Real-Time Data Integration

Floods evolve quickly. The system ingests live, authoritative data from global sources, including

• USGS Water Services – streamflow and gage-height data
• NOAA National Water Prediction Service – official forecasts and alerts
• Open-Meteo Flood API – river-discharge modeling
• OpenWeatherMap – precipitation and meteorological context
  

All data streams are processed asynchronously and stored in a structured flood-monitoring schema, powering trend visualization, alert prioritization, and AI-driven recommendations for decision-makers.

Enterprise & Security-Ready for Sovereign Deployment

Designed for on-premises and air-gapped environments, the Flood Prediction Blueprint ensures compliance with strict data-protection standards.

• Authentication & Authorization: OIDC/OAuth2 with JWT
• Comprehensive Audit Logs: Traceable and compliance-ready
• Scalable Infrastructure: Redis-backed load-balanced deployment across NVIDIA and H2O models
• REST APIs & Webhooks: Integrate with emergency-management or GIS systems

Ideal for federal agencies and critical-infrastructure operators, where reliability and privacy are non-negotiable.

Beyond Floods: Extending the Blueprint

The same architectural pattern can power a wide range of AI use for public-safety applications:

• Wildfire Prediction: Combine satellite imagery and weather data to forecast spread.
• Dam Integrity Monitoring: Analyze IoT sensor data for stress and early-warning signs.
  
• Urban Drainage Optimization: Model runoff to improve stormwater management.
  
• Transportation Resilience: Protect railways, airports, and highways from climate-driven disruptions.
  

By integrating geospatial, meteorological, and infrastructure data, developers can adapt this architecture to anticipate, plan, and respond to complex climate events.

Get Started

Explore the live demo and open-source repository:

• Demo: flood-prediction.cloud-dev.h2o.dev
• GitHub: github.com/h2oai/h2oai-flood-prediction

About the Collaboration

The H2O.ai Flood Intelligence Blueprint, accelerated by NVIDIA is part of a growing library of reference architectures that combine NVIDIA accelerated compute with H2O.ai’s enterprise GenAI platform. These blueprints enable developers, data scientists, and government agencies to fast-track resilient AI applications — from climate-risk modeling to infrastructure protection.  This blueprint powered by H2O.ai h2oGPTe end-to-end agentic AI platform is validated for the NVIDIA AI Factory for Government - a full-stack validated design that provides guidance for deploying and managing multiple AI workloads on-premises and in the hybrid cloud while meeting the compliance needs of high-assurance organizations.