01 / Problem
What is the scientific problem?
Forest fires threaten communities and ecosystems in Santander, while the environmental and meteorological indicators needed for prevention are distributed across separate public datasets.
02 / Motivation
Why does it matter?
Integrating open environmental data can help decision-makers identify vulnerable areas before seasonal conditions intensify fire risk.
03 / Method
How the problem is approached
FIRENET combines geospatial technology with open precipitation, temperature, and environmental-crime data. Convolutional and recurrent neural networks are used to forecast monthly temperature and precipitation, supporting the prioritization of areas during dry periods.
04 / Results
What the work establishes
The platform was awarded second place nationally in Colombia’s Datos a la U competition. Public reporting states that it can identify areas susceptible to forest fires and support regional prioritization.
05 / Interactive gallery
Figures, datasets, and videos
No verified public figures are currently attached to this case study.
06 / Future work
Where the project goes next
A verified technical account of this aspect is not yet available in the public record.
Research artifacts
2 sources documented above.