TerraMind-AI
TerraMind AI: Landslide Prediction & Early Warning System
📖 Table of Contents
- Problem Statement
- Proposed Solution
- System Architecture
- Technology Stack
- Implementation Plan
- Expected Outcomes
- Challenges & Considerations
- Contributors
- License
🚨 Problem Statement
Current landslide Early Warning Systems (EWS) have significant limitations:
- No Real-Time Prediction:
- Prohibitively Expensive:
- Dense Sensor Reliance: Requires an impractical density of sensors for accurate monitoring, especially in remote areas.
- Internet Dependency: Many modern systems require constant internet connectivity, which is unreliable in vulnerable regions.
💡 Proposed Solution
TerraMind AI proposes a holistic, four-pillar architecture designed to overcome these challenges:
| Pillar | Component | Description | Key Strength |
|---|---|---|---|
| 1 | 🤖 ML Prediction Model | A model designed to predict regional landslide susceptibility using historical data, satellite imagery, and terrain features (slope, elevation, rainfall). | Scalability & Low-Cost |
| 2 | 🚁 Drone-based Validation | A proposed system using UAV photogrammetry to visually identify erosion, cracks, and soil shifts in high-risk zones identified by the ML model. | Visual Ground-Truthing |
| 3 | 📟 IoT Sensor Network | A design for a low-cost sensor node (ESP32) to measure vibration, tilt, and soil moisture. Data is proposed to be transmitted via long-range, low-power LoRaWAN communication. | Real-Time, Hyper-Local Data. No Internet Needed. |
| 4 | ⚠️ SMS Alert System | A plan to integrate risk data and real-time sensor data to trigger automated SMS alerts to communities via APIs and local telecom providers. | Life-Saving Alerts |
🏗 System Architecture
(We will add a simple diagram here in the next step. For now, let’s describe it.)
The proposed data flow is designed as follows:
- Data Acquisition: Satellite data (rainfall, elevation) and historical landslide data are collected for model training.
- Risk Analysis: The ML model processes this data to generate a regional landslide susceptibility map.
- Targeted Deployment: High-risk zones from the map are prioritized for the deployment of the IoT sensor network and drone surveillance.
- Real-Time Monitoring: Sensors transmit tilt, vibration, and moisture data via LoRaWAN to a gateway.
- Data Fusion & Alerting: Sensor data is combined with the ML model’s risk assessment. If thresholds are exceeded, an alert is triggered.
- Community Warning: The alert system sends SMS messages to registered community members and authorities.
⚙️ Technology Stack
| Component | Technology & Tools |
|---|---|
| ML Model | Python, Pandas, NumPy, Scikit-learn |
| Geospatial Analysis | QGIS, Google Earth Engine |
| IoT Hardware (Proposed) | ESP32 Microcontroller, MPU6050 Accelerometer, Capacitive Soil Moisture Sensor, LoRa RA-02 Module |
| IoT Communication (Proposed) | LoRaWAN (The Things Network) |
| Drone Imaging (Proposed) | DJI Mavic Mini, WebODM (OpenDroneMap) |
| Alert System (Proposed) | Twilio API |
| Visualization (Proposed) | Grafana |
🗺 Implementation Plan
- Phase 1: Data Acquisition & ML Model Development
- Source historical landslide and rainfall data from public repositories (e.g., NOAA, USGS).
- Acquire satellite imagery and Digital Elevation Model (DEM) data for a target region.
- Develop and train a predictive model (e.g., Random Forest classifier) in Python to create a susceptibility map.
- Phase 2: Hardware Prototyping
- Assemble a prototype sensor node using an ESP32 DevKit and required sensors.
- Develop and test firmware for reading sensors and transmitting data via LoRa.
- Conduct range and power consumption tests for the LoRa module.
- Phase 3: Field Validation & Deployment
- Select a small, high-risk test area based on the ML model output.
- Perform a drone survey to capture baseline imagery.
- Deploy a pilot network of sensor nodes for a defined study period.
- Phase 4: System Integration & Alerting
- Develop a backend server to receive and store LoRa data.
- Implement the alert logic based on fused ML and sensor data.
- Integrate with the Twilio SMS API for testing the alert mechanism.
📈 Expected Outcomes
Based on the design and research, this system is projected to:
- Reduce Cost: The Bill of Materials (BOM) for a single sensor node is estimated at <$35, a significant reduction compared to commercial systems.
- Increase Accessibility: By eliminating the need for internet and dense sensor networks, the proposed architecture makes deployment in remote areas technically and economically feasible.
- Improve Warning Time: The integration of real-time physical sensors with predictive ML models is designed to provide earlier, more localized warnings than rainfall-threshold-based systems.
⚠️ Challenges & Considerations
- Sensor Calibration: Field calibrating the tilt and vibration sensors to distinguish between a landslide and everyday events (like an animal bumping the node) is a known challenge.
- Power Management: Achieving multi-month battery life will require sophisticated sleep cycles and potentially solar harvesting.
- Model Accuracy: The ML model’s accuracy is dependent on the quality and quantity of historical data, which may be scarce in some regions.
- Community Integration: The success of any EWS depends on community trust and preparedness. This design must be paired with community education programs.
👥 Contributors
- [NgawangTharchinSherpa] - Project Lead, Systems Architecture & Design
📜 License
This project is open source and available under the MIT License.