Improving Wildfire Simulations via Geometric Primitive Analysis in Noisy Crowdsourced Data
Abstract
1. Introduction
- Correlate user-submitted data regarding geometric primitives (e.g., matching similar shapes in the spatial and time domains), taking into account the human aspects of the submitters and the corresponding data time stamps.
- Mitigate the impact of inconsistent and ambiguous user-submitted measurements on the simulation engine.
- Simplify the user-submitted modeled environment to meet real-time constraints (e.g., remove duplicate entries) for the wildfire environment, to reduce the simulation time, latency, and network bandwidth required to exchange them.
- Implicitly derive additional knowledge, extracting information from the changing nature of the ontologies involved in a wildfire environment.
Contributions
2. Materials and Methods
2.1. A Motivating Example
2.2. The ACO Family of Algorithms
2.3. An Existing ACO-Based Solution
2.4. Outline of the Proposed ACO Algorithm
2.5. A Modified Pheromone Update Model
2.6. An Illustrated Example
3. Results
4. Discussion
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
ACO | Ant Colony Optimization |
MHD | Modified Hausdorff |
WUI | Wildland–Urban Interface |
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Parameter | Value | Impact |
---|---|---|
Baseline Method | ||
MHD similarity weight | 0.7 | favors shape matching over area matching |
Area similarity weight | 0.3 | favors area matching over shape matching |
ACO | ||
Number of iterations | 1000 | exploration of search space/speed of convergence |
Number of ants | 1 | exploration of search space |
Pheromone influence (α) | 0.3 | exploration of search space |
Evaporation rate | 0.01 | exploration of search space |
Minimum pheromone | 0.1 | exploration of search space |
Recent coefficient | 0.05 | increases pheromone deposition in favor of newer solutions |
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Karakonstantis, I.; Xylomenos, G. Improving Wildfire Simulations via Geometric Primitive Analysis in Noisy Crowdsourced Data. Appl. Sci. 2025, 15, 8844. https://doi.org/10.3390/app15168844
Karakonstantis I, Xylomenos G. Improving Wildfire Simulations via Geometric Primitive Analysis in Noisy Crowdsourced Data. Applied Sciences. 2025; 15(16):8844. https://doi.org/10.3390/app15168844
Chicago/Turabian StyleKarakonstantis, Ioannis, and George Xylomenos. 2025. "Improving Wildfire Simulations via Geometric Primitive Analysis in Noisy Crowdsourced Data" Applied Sciences 15, no. 16: 8844. https://doi.org/10.3390/app15168844
APA StyleKarakonstantis, I., & Xylomenos, G. (2025). Improving Wildfire Simulations via Geometric Primitive Analysis in Noisy Crowdsourced Data. Applied Sciences, 15(16), 8844. https://doi.org/10.3390/app15168844