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06_Ocean_Sentinels H

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06_Ocean_Sentinels H

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  1. Problem Statement ID – 25039 • Problem Statement Title- Integrated Platform for Crowdsourced Ocean Hazard Reporting and Social Media Analytics • Theme- Disaster Management • PS Category- Software • Team ID- • Team Name- Ocean Sentinels SMART INDIA HACKATHON 2025 TITLE PAGE

  2. IDEA TITLE Ocean Sentinels • Proposed Solution • Mobile & Web App: Geotagged hazard reporting with images/videos. • Map Dashboard: Live reports with location, type, time, severity. • Social Media + NLP: Real-time keyword detection and sentiment analysis. • Backend + APIs: Central database with INCOIS integration. • Role-based & Offline: User levels with offline data sync. • How it addresses the problem • Fills Data Gaps: Real-time citizen field reports. • Faster Response: Instant alerts for authorities. • Boosts Accuracy: Validates INCOIS warning models. • Situational Awareness: Centralized live dashboard. • Community Involvement: Engages locals in hazard reporting. • Innovation and uniqueness of the solution • Crowdsourcing + Social Media Fusion: Combines citizen reports and social media insights. • Real-time NLP Trend Analysis: Detects keywords, urgency, and sentiment instantly. • Dynamic Hotspot Generation: Highlights hazard clusters on live maps. • Offline-first Reporting System: Works without internet, syncs data later. @SIH Idea submission- Template

  3. TECHNICAL APPROACH Ocean Sentinels • Tech Stack Overview • Backend: • FastAPI (Python) → REST + WebSocket APIs • PostgreSQL (Neon) → Cloud-hosted relational database for reports, users, hazard data • Celery+Redis/Kafka → Background tasks for Social media ingestion, NLP processing and Threat scoring • WebSocket / Socket.IO → Real-time updates: map, alerts, live location • Twilio / SMS API → Emergency alert SMS • Hugging Face Transformers(BERT, RoBERTa, XLM-R)/custom ML models → NLP & predictive threat scoring • OpenCV → Image/video hazard classification • Frontend • React + Vite → SPA • TailwindCSS + ShadCN/UI → Reusable components, styling • Recharts / Chart.js → Analytics dashboard • Leaflet.js / Mapbox GL JS → Interactive map visualization (Red Zone, Green Zone, citizen reports) • React Hook Form → Multi-step reporting form • Zustand / React Hooks → State management • WebSockets → Live location & alerts updates • Predictive Analytics • Long Short-Term Memory (LSTM) → Sequence modeling for temporal hazard data • AutoRegressive Integrated Moving Average (ARIMA) → Time-series forecasting for risk trend prediction • DBSCAN → Spatial clustering of hazard reports to detect red zones • Social Media Integration • Twitter API / YouTube API / Facebook Graph API → Fetch public posts/comments • NLP pipeline: 1. Filter hazard-related content 2. Calculate threat score 3. Notify analysts • Predictive Red Zone generation → Based on verified reports and historical data @SIH Idea submission- Template

  4. FEASIBILITY AND VIABILITY Ocean Sentinels • UI Screenshots: LINK 📈 Adoption Model 🛰️Pilot (Free): Deployed in coastal villages & fishing communities with support from INCOIS & local NGOs to validate real-time hazard reporting. 🏛️Government Integration: Adoptable by INCOIS & NDMA as a unified hub for two-way communication, citizen reports, satellite/LoRa inputs, and social media analytics. 🌍 Expansion: Scalable to multi-hazard use (floods, landslides, wildfires) → creating a nationwide disaster management ecosystem. @SIH Idea submission- Template

  5. IMPACT AND BENEFITS Ocean Sentinels Salient Features: Impacts: Increase hazard awareness & prep rate by 65 to 75% Faster alerts help reduce response time by 60 to 80% Strengthen local–volunteer–agency coordination Build trust with real-time transparency Benefits: Centralized Hazard Reporting. Early Warning & Rapid Alerts Data Collection & Decision Support Predictive Risk Analysis. REPORT:LINK @SIH Idea submission- Template

  6. RESEARCH AND REFERENCES Ocean Sentinels • References • UNESCO/IOC Tsunami Early Warning Systems – Research on ocean monitoring frameworks. • IPCC Climate Reports (2021/2022) – Climate-driven rise in coastal hazards. • NDMA India Guidelines on Coastal Hazards – National Disaster Management Authority (India). • NOAA Tsunami Research – U.S. National Oceanic & Atmospheric Administration (hazard forecasting models). • Sakaki et al., Earthquake Shakes Twitter Users: Real-time Event Detection by Social Sensors (WWW 2010). • Recent IEEE/ACM papers on NLP for disaster response & sentiment analysis. @SIH Idea submission- Template

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