MODELING OF WILDFIRE RISKS AND OPTIMIZATION OF COMBAT STRUCTURE ALLOCATION THROUGH ARTIFICIAL
INTELLIGENCE TECHNIQUES
Name: JÉFERSON PEREIRA MARTINS SILVA
Publication date: 31/08/2023
Examining board:
Name |
Role |
|---|---|
| GILSON FERNANDES DA SILVA | Advisor |
Summary: This study presents an approach to wildfire management integrating a WebGIS system and artificial intelligence. To train the deep learning model, data related to vegetation, topography, anthropogenic factors, and historical fire records for the year 2008 in
Andalusia, Spain were collected. The dataset was duly normalized and split into 70% for training, 10% for validation, and 20% for testing. Various algorithms and activation functions were evaluated, with the combination of Adam and Relu standing out,
recording an accuracy of 0.86 during training. Based on this model, a risk map was generated. By applying the K-means method to this map, high-risk areas were identified, and central points for the installation of firefighting infrastructures were
suggested. To validate the model's efficacy, the suggested positions were compared with the actual locations of firefighting aircraft in Andalusia, Spain. With 31 clusters and a risk threshold of 0.75, the proximity between the proposed coordinates and the actual ones was notable, reinforcing the practical potential of the approach proposed in this study.
Keywords: Forest protection, Machine learning, Operations research, Climate change.
