OUR
TECHNOLOGY

Synthetic Aperture Radar (SAR) satellite

Frequency: 6-12 days
Data Points
- Soil moisture levels
- Surface water extent
- Ground movement/flooding
- Crop structure

Landsat-8 Satellite

Optical imaging satellite
Frequency: 16-day 
Key Data Bands Used:
- Band 4 (Red): For vegetation analysis
- Band 5 (Near-Infrared): For water detection
- Band 6 (SWIR 1): For moisture content
- Band 7 (SWIR 2): For cloud detection

Machine Learning Components

CNN (Convolutional Neural Network)

Purpose: Analyze satellite images to detect features like water bodies and field boundaries.

Key Layers: -

Convolution Layers: Detect intricate features within the images.

Pooling Layers: Reduce image size while retaining important information.

Dense Layers: Make final flood probability predictions.- Input: 256x256 pixel satellite image tiles.

Output: Flood probability (0-1).

LSTM (Long Short-Term Memory)

Purpose: Analyze time-series data to predict flood events.

- Input Data Includes:

  - Daily rainfall

  - River levels

  - Soil moisture

  - Previous flood events

Sequence Length: 30 days

Output: Flood probability for the next 72 hours.

Financial Metrics

Risk Score Calculation

- Components:
   - Historical flood frequency (40% weight)
   - Current flood risk (40% weight)
   - Crop health score (20% weight)
- Range: 0-1 (0 = lowest risk, 1 = highest risk)

Insurance Premium Factor

 - Formula: Base premium × (1 + risk score)
- Example: If risk score is 0.3, premium factor is 1.3