Estimating Fire Response Times and Planning Optimal Routes Using GIS and Machine Learning Techniques
Yazarlar (2)
Dr. Öğr. Üyesi Tuğrul URFALI
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Erciyes Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
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| Makale Alt Türü | ESCI dergilerinde yayınlanan tam makale |
| Dergi Adı | Geomatics |
| Dergi ISSN | 2673-7418 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | ESCI |
| Makale Dili | İngilizce |
| Basım Tarihi | 10-2025 |
| Cilt No | 5 |
| Sayı | 4 |
| Sayfalar | 19 / 0 |
| DOI Numarası | 10.3390/geomatics5040058 |
| Makale Linki | https://doi.org/10.3390/geomatics5040058 |
| Özet |
| Highlights What are the main findings? An end-to-end framework that fuses GIS-derived network features, eXtreme Gradient Boosting (XGBoost), Support Vector Machines, and Dijkstra routing accurately predicts dispatch-to-arrival times and maps fastest fire-response routes for 7421 cleaned incidents in Kayseri. XGBoost attains 78.41% accuracy within ±3 min (MAE ≈ 1.67 min, R2 ≈ 0.46), outperforming SVR, while GIS service-area maps reveal that peripheral districts lie beyond the 10-min reach of current stations. What is the implication of the main finding? Fire services gain a real-time, data-driven tool that pairs precise time forecasts with optimal paths, enabling faster, evidence-based deployment and resource reallocation. Urban planners and emergency managers can use the scalable GIS-ML workflow to identify coverage gaps, site new stations strategically, and ultimately improve public safety by reducing response delays. Abstract This study proposes an integrated, data-driven framework that couples Geographic Information Systems (GIS) with machine-learning techniques to improve fire-department response efficiency in an urban setting. Using an initial archive of 10,421 geocoded fire incident reports collected in Kayseri, Turkey (2018–2023), together with an OpenStreetMap-derived road network, we first generated an “ideal route-time” feature for every incident via Dijkstra shortest-path analysis. After data cleaning and routability checks … |
| Anahtar Kelimeler |
| Dijkstra algorithm | emergency management | fire response | Geographic Information Systems (GIS) | route optimization | Support Vector Machines (SVM) | XGBoost |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 1 |
| SCOPUS | 1 |
| Google Scholar | 1 |