Predict. Preserve. Prosper.
FlowSense is a smart river monitoring and prediction system that combines AI, IoT, Neural Networks, and Geospatial Modeling to analyze shifting river flow patterns in major river systems like the Amazon, Nile, and Yangtze.
Our mission is to detect naturally split-off tributaries and repurpose untapped water sources for rural water security, agriculture, and ecosystem rebalancing — all through real-time sensing, data-driven planning, and predictive intelligence.
- 📡 Real-time Monitoring with IoT-based sensors
- 🧠 AI/ML-Powered Flow Prediction (LSTM, CNN)
- 🗺️ Geospatial Analysis of breakoff patterns (SWAT+GIS)
- 🌾 Water Reallocation Planner for agriculture & drinking needs
- 🔁 River Reconnection Engine to redirect flow downstream
- 📊 Rural Water Security Index for policy impact
- 🔗 APIs & Dashboards for government & NGOs
| River | Region | Challenges | Focus |
|---|---|---|---|
| Amazon | South America | Deforestation, shifting sediments | Tributary formation & reallocation |
| Nile | Africa | Political water conflict, new dam impacts | Water flow forecasting & policy modeling |
| Yangtze | China | Damming, flood control, soil erosion | Downstream flow optimization |
| Category | Tools & Frameworks |
|---|---|
| Sensors | Raspberry Pi, Flow Meters, Rainfall Gauges, Soil Moisture Probes |
| Cloud & Data | AWS IoT Core, Google Earth Engine, India WRIS, NASA, MODIS, IMD |
| AI/ML | TensorFlow, PyTorch, LSTM, CNN, XGBoost, Keras |
| Hydrology | SWAT+, ArcGIS, QGIS, Soil & Sedimentation Models |
| Backend/API | Flask, FastAPI, AWS API Gateway |
| Visualization | Streamlit, Dash, Grafana, Plotly |
[ Satellite & IoT Data ] ---> [ Preprocessing Layer ]
--> [ SWAT+GIS Modeling ]
--> [ LSTM/ML Models ]
--> [ Breakage + Path Prediction ]
--> [ Water Allocation & Alert System ]
--> [ Dashboards & APIs ]
See full architecture diagram here — (include generated image link)
Predict stress zones and high erosion areas using sedimentation and rainfall data.
Simulate new paths using LSTM models trained on historical & seasonal patterns.
Plan micro-dams, local water storage, and redirection systems for rural needs.
Design infrastructure to reintroduce excess water into main flow downstream.
- ✅ Pilot Deployment in Indian River Basins (Godavari, Narmada)
- ✅ National Dashboard for Water Deviation Monitoring
- ✅ NGO & State-Level Integration for rural outreach
- ✅ Global Research Extension to Nile & Amazon Deltas
| Endpoint | Description |
|---|---|
/predict-breakage |
Returns zones at high risk of tributary formation |
/suggest-paths |
Suggests probable new paths of river flow |
/allocate-water |
Recommends use cases for available split water |
/reconnect-path |
Simulates optimal rejoining flow path |
/get-security-index |
Fetches Rural Water Security Index (RWSI) for a region |
git clone https://github.com/your-org/FlowSense.git
cd FlowSense
pip install -r requirements.txt
# Start local sensor + AI pipeline
python app.py- SWAT+ for Hydrological Modeling
- "Spatiotemporal Analysis of River Path Dynamics" – Journal of Hydrology
- "Sediment Flow and River Behavior" – Elsevier
- ISRO Bhuvan
- Indian Meteorological Department (IMD)
- Rural Agriculture Ministry
- Local Panchayat Networks
- AWS Cloud Credits for Research
- 💧 Potential to secure water for 100+ rural districts
- 🌾 Enhance agricultural yield in rain-fed areas
- 🧠 Introduce explainable AI into hydrology for policy impact
- 🛰️ A model for global river delta monitoring
We welcome hydrologists, data scientists, developers, and community partners to collaborate!
# Fork, clone, and start a new feature branch
git checkout -b feature/my-contributionRead CONTRIBUTING.md for full guidelines.
This project is licensed under the MIT License - see the LICENSE file for details.
Special thanks to:
- ISRO, NASA, and NOAA for satellite data
- Farmers and water NGOs in India and Brazil
- Global Water Partnership for guidance
📧 Email: flowsense@yourdomain.org
🌐 Website: www.flowsense.ai
📍 Location: Global HQ – Bangalore, India | Field Sites – Ethiopia, Brazil, China
FlowSense — Because Water Shouldn't Be Wasted, It Should Be Watched.