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Predictive Probability Density Mapping for Search and Rescue Using An Agent-Based Approach with Sparse Data: Simulation Data
- Citation Author(s):
-
Jan-HendrikEwers
- Submitted by:
- Jan-Hendrik Ewers
- Last updated:
- Mon, 11/04/2024 - 14:34
- DOI:
- 10.21227/1t3y-3y89
- Data Format:
- License:
5 Views- Categories:
- Keywords:
Abstract
Predicting the location where a lost person could be found is crucial for
search and rescue operations with limited resources.
To improve the
precision and efficiency of these predictions,
simulated agents can be
created to emulate the behavior of the lost person.
Within this study,
we introduce an innovative agent-based model designed
to replicate diverse psychological profiles of lost persons,
allowing these agents to navigate real-world landscapes while making
decisions autonomously
without the need for location-specific training.
The probability distribution map depicting the potential location of the lost person
emerges through a combination of Monte Carlo simulations and
mobility-time-based sampling.
Validation of the model is achieved using real-world Search and Rescue data to train a Gaussian Process model.
This allows generalization of the data to sample initial starting points for the agents during validation.
Comparative analysis with historical data
showcases promising outcomes relative to alternative methods.
This work introduces a flexible agent that can be employed in search and rescue operations,
offering adaptability across various geographical locations.
Creating the backup
The following was used to create the backup and is presented here for future debuging purposes.
```bash
docker-compose restart db; sleep 3; docker exec -it jpdmgen_db_1 pg_dump -U postgres -d jpdmgen | tee jpdmgen_20231211.sql
```
Restore
```bash
# Start a postgis instance
docker run --rm --name postgres -v $PWD:/data postgis/postgis:15-latest
# Load the pg_dump
docker exec postgres creatdb -U postgres jpdmgen
docker exec psql -U postgres jpdmgen -f /data/jpdmgen_20231211.sql
```
