## 6 Conclusions  
This study presented a comparative evaluation of three AI-based models for wildfire prediction: a Convolutional Neural Network, Transformer architecture, and Actor-Critic Deep Reinforcement Learning. Across 100 experimental runs, all models achieved excellent predictive performance, with accuracy, precision, recall, and F1-Scores approaching 1.0. The Transformer model demonstrated marginally superior generalization capabilities, supported by its attention mechanism for capturing spatiotemporal patterns. These results underscore the value of deep learning techniques in enhancing early warning systems and optimizing wildfire management strategies. In Chile and other fire-prone regions, such models can assist in proactive resource allocation and risk mitigation. Their global relevance is further reinforced by the increasing frequency and intensity of wildfires driven by climate change.  
While the models performed robustly, several limitations should be addressed. Validation using geographically diverse datasets is necessary to ensure generalizability. Additionally, the computational demands of Transformer architectures may hinder real-time implementation in low-resource settings. Exploring lightweight alternatives, such as optimized CNNs or hybrid approaches, is recommended. Future research will focus on multi-modal data integration--including satellite imagery, real-time meteorological inputs, and land use data--to improve forecasting precision. Expanding model outputs to include fire spread and intensity predictions could also provide critical insights for emergency response planning.  
In summary, this work establishes a foundation for integrating deep learning methods into operational wildfire prediction systems. Continued refinement and contextual adaptation will enhance their impact on sustainable environmental management and disaster preparedness worldwide.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073990/s1](https://www.mdpi.com/article/10.3390/app15073990/s1)]([https://www.mdpi.com/article/10.3390/app15073990/s1](https://www.mdpi.com/article/10.3390/app15073990/s1)), Algorithm S1: Actor-Critic Training for Wildfire Prediction; Algorithm S2: CNN Training for Wildfire Prediction; Algorithm S3: Transformer Training for Wildfire Prediction.  
Conceptualization, R.P., M.C.-S., C.S. and A.I.; methodology, B.I.; software, C.V.-S. and C.d.I.F.; validation, V.D., B.I. and C.V.-S.; formal analysis, R.P., M.C.-S., C.S. and A.I.; resources, R.P., M.C.-S., C.S. and A.I.; data curation, C.V.-S.; writing--original draft preparation, C.V.-S.; writing--review and editing, M.C.-S.; visualization, C.S.; supervision, V.D.; project administration, R.P., M.C.-S. and C.S.; funding acquisition, A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding  
Not applicable  
Datasets and Python Scripts are available in [[https://github.com/cvidalmsu/FONDEF-IDEA-](https://github.com/cvidalmsu/FONDEF-IDEA-)]([https://github.com/cvidalmsu/FONDEF-IDEA-](https://github.com/cvidalmsu/FONDEF-IDEA-)), accessed on 30 March 2025.  
We are very grateful to the Department of Forest Fire Protection from CONAF of the Maule Region, Chile, for providing the information and being part of this Project's team. Also, the authors gratefully acknowledge the support provided by the ANID BASAL Center FB210015 (CENAMAD).  
The authors declare no conflicts of interest.