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Flood Risk Analysis and Affected Areas
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Inspiration

Natural disasters, especially floods, can cause serious damage to communities and put lives at risk. During emergencies, rescue teams must quickly identify dangerous areas, locate victims, and find safe rescue routes.

We were inspired to develop Geo-Rescue-Bot, an AI-assisted disaster response and rescue simulation system that combines flood risk analysis, intelligent pathfinding, and constraint-based rescue planning.

Our goal is to demonstrate how artificial intelligence and optimization algorithms can support faster, safer, and smarter emergency response decisions.

What it does

Geo-Rescue-Bot is an interactive flood disaster simulation platform that helps users understand flood risks and plan rescue operations.

The system provides flood simulation, flood risk analysis, intelligent rescue routing, constraint-based mission planning, dynamic hazard handling, and an interactive web dashboard.

It uses BFS, DFS, and bidirectional BFS algorithms to explore rescue routes. Constraint Satisfaction Problem (CSP) techniques help prioritize victims and account for safety, energy, and time limitations.

The project also integrates a Google Gemini AI assistant architecture with six specialized backend tools for natural-language interaction with rescue and flood analysis functions.

How we built it

We developed Geo-Rescue-Bot using Python and Flask for the backend, HTML, CSS, and JavaScript for the interactive dashboard, and Google Gemini API for AI integration.

We implemented search algorithms for rescue route exploration and CSP techniques for mission planning.

The flood simulation evaluates environmental conditions and identifies potentially hazardous locations. The rescue system then uses this information to calculate feasible rescue plans.

We connected the backend services to the browser interface through REST APIs and created automated tests to validate the system.

Challenges we ran into

One of our biggest challenges was integrating multiple components, including the AI assistant, flood simulation, rescue algorithms, and interactive dashboard.

Rescue planning was particularly challenging because routes must consider obstacles, changing hazards, victim priorities, limited energy, and mission deadlines.

We also encountered Google Gemini API Free-tier quota restrictions and request timeouts during live AI testing.

Another challenge was ensuring smooth communication between the Flask backend and frontend interface.

Accomplishments that we're proud of

We successfully developed an integrated disaster response simulation combining flood analysis, intelligent rescue planning, and an interactive user interface.

We implemented multiple search algorithms, six CSP constraints, and dynamic route re-evaluation when hazards change.

Our rescue-planning tests successfully completed 45 out of 45 CSP-driven rescue missions across multiple algorithms and simulation scenarios.

We also integrated a Gemini-based AI assistant architecture with six specialized tools.

What we learned

We learned how artificial intelligence, search algorithms, constraint satisfaction, and environmental simulation can work together to address complex rescue-planning problems.

We gained experience in Python, Flask APIs, AI tool calling, frontend integration, debugging, and automated testing.

We also learned the importance of building reliable systems that remain functional when external AI services experience rate limits or temporary failures.

What's next for Geo-Rescue-Bot

We plan to expand Geo-Rescue-Bot with real-time weather information, satellite imagery, GIS datasets, more advanced flood prediction, multi-agent rescue coordination, and cloud deployment.

Our long-term vision is to develop Geo-Rescue-Bot into an AI-assisted disaster response decision-support platform that could help emergency teams make informed decisions and potentially save lives.

Geo-Rescue-Bot — Predict Risks. Plan Smarter. Rescue Safer.