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Öğe Expanding the Accessible Forest Areas by Improving Forest Road Standards and Utilizing Mobile Fire-fighting Teams(Forest Engineering and Technologies Platform, 2024) Kasap, Caner Yavuz; Akay, Abdullah Emin; Aricak, Burak; Bilici, Ebru; Uçar, Zennure; Erkan, Neşat; Guney, Coskun OkanIn Turkiye, insufficient technical standards of the forest roads limit the speed of the fire truck, leading to increase in the arrival time of the initial response team to the fire areas. Improving forest road standards will increase the design speed and expand the accessible forest areas within the critical response time. In this study, the effect of improving forest road standards on expanding accessible forest areas was investigated. Considering the forest areas in Antalya Forestry Regional Directorate in Turkiye, accessible areas by the stationary initial response teams (103) and mobile teams (71) were determined from the existing road network, and then, the possible increase in the accessible forest areas was investigated when the road standards are improved. Within the scope of the study, the impact of mobile teams used in emergencies on forest areas reached during the critical response period was also evaluated. According to the results, in the scenario where current road standards and stationary teams were evaluated, it was determined that only 59.54% of the forest areas could be reached by initial response teams during the critical response time. When the road standards were improved, this rate increased to 71.69%. On the other hand, when the current road standards and stationary and mobile teams were evaluated together, it was determined that initial response teams could reach 70.40% of the forest areas during the critical response time, and if road standards were improved, this rate increased to 78.17%. Also, utilizing mobile teams increased the accessible forest areas within the critical response time by 9.03%. The results have shown that improvements in road standards and the presence of mobile teams have a very effective role in combating forest fires. © Copyright 2024 by Forest Engineering and Technologies Platform on-line at https://dergipark.org.tr/en/pub/ejfe.Öğe Mapping the probability of Forest fire in the Mediterranean region of Türkiye using the GIS-based fuzzy-AHP method(Taylor & Francis Inc, 2025) Ucar, Zennure; Guney, Coskun Okan; Akay, Abdullah E.; Bilici, Ebru; Erkan, NesatForest fires have increased in frequency, intensity, and extent significantly worldwide due to climate change and human activities, particularly in the Mediterranean region. Fire-prone areas should be determined primarily to take precautions against forest fires and reduce their ecological, economic, and social impact. This study aimed to determine the spatial distribution of forest fire probability for the Antalya Regional Directorate of Forestry (RDF), highly vulnerable to forest fires. The forest fire probability map was generated using the GIS-based Fuzzy-AHP method, prioritizing decision criteria, including stand characteristics, topographic features, meteorological parameters, and proximity to anthropogenic structures. The results indicated that the most important factors influencing the fire were tree species, development stage, and proximity to road networks. The generated map showed that 45.82% of the forests in Antalya RDF were in the very high class, while 15.82% were in the high-level class. The fire probability map, validated using the Area Under Curve (AUC) method, offered promising and acceptable results above 0.7. The Fuzzy-AHP method, when integrated with GIS techniques, effectively predicts fire probability levels in fire-sensitive forests. This method will empower fire managers to develop and implement strategies that enhance forest fire resilience by predicting areas with high fire probability.Öğe Using Machine Learning Algorithms to Predict Forest Fire Probability in Mediterranean Region of Türkiye(Aves, 2025) Bektas, Aybike Goksu; Karas, Ismail R.; Akay, Abdullah Emin; Guney, Coskun Okan; Ucar, Zennure; Bilici, Ebru; Erkan, NesatDetermining the forest fire probability levels by analyzing the main fire factors can provide forest managers with the basis for making critical decisions on issues such as fire prevention strategies, fuel management, fire safety measures, emergency planning, and placement of firefighting teams. The main fire influencing factors, including vegetation factors, topographical factors, climate factors, and proximity to some features such as roads and residential areas, have been considered to generate forest fire probability maps. The machine learning (ML) algorithms have become an effective tool in predicting forest fire probability. This study aimed to generate a forest fire probability map by using two commonly used ML models, logistic regression (LR) and support vector machines (SVMs), integrated with Geographical Information System (GIS) techniques. The study was implemented in & Scedil;elale Forest Enterprise Chief (FEC) located in the Mediterranean city of Antalya in T & uuml;rkiye. In the study, the fire influencing factors were tree species, crown closure, tree stage, slope, aspect, and distance to roads. The forest fires that occurred from 2001 to 2021 in & Scedil;elale FEC was considered in the training stage of the models. The accuracy of the fire probability maps was verified using the area under curve (AUC) value. As a result of performing the ML models, estimations were made for 47 086 points on the map which were categorized into five fire probability levels (very high, high, medium, low, and very low). The results showed that the accuracy of the fire probability map generated by the LR model was better (AUC = 0.845) than the accuracy of map generated by the SVM model (AUC = 0.748). According to the probability maps, more than half of the forests had very high/high fire probability levels in the study area.Öğe Valuing Improved Firefighting Access for Wildfire Damage Prevention in Mediterranean Forests(Mdpi, 2025) Akay, Abdullah Emin; Erkan, Nesat; Bilici, Ebru; Ucar, Zennure; Guney, Coskun OkanTo effectively combat wildfires, ground teams must reach the fire site via road network within critical response time. However, low-standard forest roads can reduce firetruck speeds and delay fire response times. This study aimed to investigate how improving road standards affects firefighting access within critical response time and contributes to reducing timber losses. This study was conducted in Antalya, the city most affected by wildfires in T & uuml;rkiye. In the study, highly fire-prone forests were first identified based on a fire probability map of Antalya, developed through a GIS-based MCDA model incorporating the Fuzzy-AHP method. Then, the highly fire-prone forests and their corresponding timber volume were determined. Finally, the economic value of timber saved from fire and the present net value of total road costs were determined. As a result of improving forest roads, the forest areas that could be reached in time increased by 11.04%, making an additional 81,867.53 hectare of highly fire-prone forests accessible. The amount and economic value of timber products saved in this area were 971,195.55 m3 and 37,689,301, respectively. The cost of improved roads was 37,386,622 while the resulting positive net economic value of 302,679 indicates that investing in forest roads improvements is a viable option.
