Satellite Imaging for Disaster Prediction Based on AI
DTP’s AI-based systems focusing on scanning images obtained from satellites orbiting the earth, and look for any changes that could help to predict the occurrence of a natural disaster like landslides, floods, volcanic eruptions, and tsunamis. AI-based system would make the predictions through a combination of machine learning, rainfall records, and flood simulations. AI and ML systems could potentially save millions of lives when it comes to managing, early-detecting, and predicting disasters.
- Forest Fire Prediction and Monitoring
Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. Prediction must be made quickly for a large area, so remote sensing satellite data must be used as the main input data. The prediction built must be able to be verified on an ongoing basis to improve the accuracy of the predicted results. Monitoring to find out ahead of time forest fires that arise so that steps can be taken to prevent broader fires.
- Tsunami Early Warning System
A tsunami warning system (TWS) is used to detect tsunamis in advance and issue warnings to prevent loss of life and damage to property. Using AI Based model and network of sensors to detect tsunamis, an early warning system supported by communications infrastructure will issue timely alarms to permit evacuation of the coastal areas.
- Flood Prediction and Early Warning System
Floods are among the most destructive natural disasters, which are highly complex to model. DTP uses satellite images as an additional spatially distributed source of information for on-line updating of modelling systems. Algorithms were developed to assess soil moisture and flood extent from radar images. The satellite information was later on also used for floodplain mapping and evaluated for its potential use for detection and quantification (volume assessment) of water detention areas intended for floodwater storage.