UAV-based forest fire early warning and intervention simulation system with high-accuracy hybrid AI model
| dc.contributor.author | Başarslan, Muhammet Sinan | |
| dc.contributor.author | Canlı, Hikmet | |
| dc.date.accessioned | 2026-02-23T12:42:11Z | |
| dc.date.available | 2026-02-23T12:42:11Z | |
| dc.date.issued | 2026 | |
| dc.department | Fakülteler, Mühendislik Fakültesi, Yazılım Mühendisliği Bölümü | |
| dc.description.abstract | Featured Application The proposed system can be directly applied to early forest fire detection and rapid response planning using UAV-based surveillance infrastructures. By combining a high-accuracy hybrid deep learning model with a balanced drone task assignment algorithm, the system enables real-time identification of fire events and efficient allocation of available UAV resources. This approach is particularly suitable for large forest areas, national parks, and wildfire-prone regions, where fast intervention and optimal resource utilization are critical. The system can support decision-makers by reducing false alarms, minimizing response time, and improving energy-efficient deployment of firefighting drones.Abstract In this study, a hybrid deep learning model that combines the VGG16 and ResNet101V2 architectures is proposed for image-based fire detection. In addition, a balanced drone guidance algorithm is developed to efficiently assign tasks to available UAVs. In the fire detection phase, the hybrid model created by combining the VGG16 and ResNet101V2 architectures has been optimized with Global Average Pooling and layer merging techniques to increase classification success. The DeepFire dataset was used throughout the training process, achieving an extremely high accuracy rate of 99.72% and 100% precision. After fire detection, a task assignment algorithm was developed to assign existing drones to fire points at minimum cost and with balanced load distribution. This algorithm performs task assignments using the Hungarian (Kuhn-Munkres) method and cost optimization, and is adapted to direct approximately equal numbers of drones to each fire when the number of fires is less than the number of drones. The developed system was tested in a Python-based simulation environment and evaluated using performance metrics such as total intervention time, energy consumption, and task balance. The results demonstrate that the proposed hybrid model provides highly accurate fire detection and that the task assignment system creates balanced and efficient intervention scenarios. | |
| dc.identifier.doi | 10.3390/app16031201 | |
| dc.identifier.issn | 2076-3417 | |
| dc.identifier.issue | 3 | |
| dc.identifier.scopus | 2-s2.0-105030076871 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.uri | https://doi.org/10.3390/app16031201 | |
| dc.identifier.uri | https://hdl.handle.net/11501/2650 | |
| dc.identifier.volume | 16 | |
| dc.identifier.wos | WOS:001687571000001 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Canlı, Hikmet | |
| dc.institutionauthorid | 0000-0003-3394-7113 | |
| dc.language.iso | en | |
| dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | |
| dc.relation.ispartof | Applied Sciences (Switzerland) | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.subject | Forest Fire | |
| dc.subject | Hybrid Learning | |
| dc.subject | Real-Time Task Assignment | |
| dc.subject | ResNet101V2 | |
| dc.subject | VGG16 | |
| dc.title | UAV-based forest fire early warning and intervention simulation system with high-accuracy hybrid AI model | |
| dc.type | Article |
