Glofsense is a cutting-edge platform designed for the early prediction of Glacial Lake Outburst Floods (GLOFs) in the Hindukush region. It leverages multiple data sources and advanced analytics to provide early warnings and insights into potential flooding events.
Glofsense predicts GLOFs using the following three approaches:
- Sensor Deployment: Sensors are installed in glacial lakes to collect real-time data.
- Predictive Analytics: A machine learning model analyzes sensor data to detect anomalies and forecast potential floods.
- Sensor Types:
- Floating Sensors: Altitude, Humidity, Latitude, Longitude, Temperature, Water Temperature, Gyroscope (X, Y, Z axes).
- Moraine-Attached Sensors: Humidity, Temperature, Vibration, Flow Velocity.
- Uses Sentinel-1 GRD SAR imagery.
- Analyzes Gamma0 backscatter values to track glacial lake changes.
- Machine learning models predict flood risk based on SAR imagery patterns.
- Uses Digital Elevation Models (DEM) to simulate water flow from glacial lakes.
- Analyzes TIFF files to evaluate slopes, water accumulation, and drainage patterns.
- Predicts potential flood paths based on topography.
- Real-time monitoring through live camera feeds placed near glacial lakes.
- Identifies visual changes in terrain, ice melting, or other environmental shifts.
- Multi-Source Data Fusion: Combines sensor data, SAR images, DEM analysis, and camera feeds.
- Real-Time Alerts: Sends early warnings in case of detected anomalies.
- Web-Based Dashboard: Interactive UI for monitoring live data, graphs, and risk assessment.
- Machine Learning Models: Utilizes XGBoost, CNNs, and GIS-based algorithms for accurate predictions.
- User-Friendly Interface: Web-based dashboard for visualization and analysis.
We welcome contributions! Feel free to fork the repository and submit pull requests.
This project is licensed under the MIT License.
For inquiries, reach out at [ni8crawler12@gmail.com] or visit our website: Glofsense.