FloodSignal — Convert News Reports to Flood Predictions

Turn news into flood warnings before sensors can

FloodSignal converts scattered news reports, social media posts, and historical narratives into quantitative flood risk predictions for regions where traditional sensor infrastructure doesn't exist. Using Claude/GPT-4 to extract structured signals from unstructured text, we validate predictions against time-series weather data to generate early warnings 12-48 hours faster than conventional methods. Emergency management agencies in underserved regions gain actionable flood intelligence without deploying expensive physical sensors.

Key Benefits:

- Fill critical data gaps in sensor-sparse regions by extracting flood signals from 10,000+ daily news sources and social media posts across multiple languages

- Generate validated predictions 12-48 hours earlier than traditional methods by combining LLM-extracted narratives with historical time-series validation

- Reduce false alarm rates by 60% through cross-validation of qualitative reports against weather patterns, enabling better resource allocation during actual emergencies

MVP Scope: Build an MVP that ingests flood narratives from news APIs and social media, uses LLM to extract structured signals (water levels, affected areas, severity), validates predictions against historical time-series data, and generates alerts for high-risk regions. Focus on 2-3 pilot regions with existing data infrastructure.

Tech Stack: Claude/GPT-4 API, Python, PostgreSQL, Redis, FastAPI, React, Pinecone/Weaviate, Selenium/Playwright, Pandas/NumPy

Components:

- News Report Crawler

- LLM-Powered Signal Extraction

- Time-Series Validation Engine

- Prediction & Alert System

- Admin Dashboard

Quality assessment: Strong real-world problem (flood prediction in sensor-sparse regions) with concrete technical approach (LLM signal extraction + time-series validation), but lacks originality in execution (standard LLM + ML pipeline) and missing critical details on validation methodology, accuracy benchmarks, and how it handles the inherent uncertainty of converting narrative text to quantitative predictions.