Version 1
: Received: 31 May 2023 / Approved: 31 May 2023 / Online: 31 May 2023 (14:29:50 CEST)
How to cite:
Aksoy, E.; Kocer, A.; Yilmaz, İ.; Akcal, A.N.; Akpinar, K. Machine Learning Method for Assessing Fire Risk in Wildland-Rural and Urban Interface Regions: The Example of Istanbul’s European Side. Preprints2023, 2023052263. https://doi.org/10.20944/preprints202305.2263.v1
Aksoy, E.; Kocer, A.; Yilmaz, İ.; Akcal, A.N.; Akpinar, K. Machine Learning Method for Assessing Fire Risk in Wildland-Rural and Urban Interface Regions: The Example of Istanbul’s European Side. Preprints 2023, 2023052263. https://doi.org/10.20944/preprints202305.2263.v1
Aksoy, E.; Kocer, A.; Yilmaz, İ.; Akcal, A.N.; Akpinar, K. Machine Learning Method for Assessing Fire Risk in Wildland-Rural and Urban Interface Regions: The Example of Istanbul’s European Side. Preprints2023, 2023052263. https://doi.org/10.20944/preprints202305.2263.v1
APA Style
Aksoy, E., Kocer, A., Yilmaz, İ., Akcal, A.N., & Akpinar, K. (2023). Machine Learning Method for Assessing Fire Risk in Wildland-Rural and Urban Interface Regions: The Example of Istanbul’s European Side. Preprints. https://doi.org/10.20944/preprints202305.2263.v1
Chicago/Turabian Style
Aksoy, E., Arif Nihat Akcal and Kudret Akpinar. 2023 "Machine Learning Method for Assessing Fire Risk in Wildland-Rural and Urban Interface Regions: The Example of Istanbul’s European Side" Preprints. https://doi.org/10.20944/preprints202305.2263.v1
Abstract
As in many parts of the world, rural-urban, forest-urban interface areas surrounding urban regions expose the natural areas they interact with to a threat of fire risk that can reach various sizes. This risk has been assessed for various regions of the world using many different methods and numerical models so far. Among these, it is seen that machine learning models have successful applications in risk assessment and risk prediction studies. For the fire risk prediction of Istanbul's yet unurbanized regions, but where the city is anticipated to potentially shift, data was collected using the opportunities provided by Geographic Information Systems and Remote Sensing technologies based on fires that occurred between 2000-2021, and the region was examined. Machine learning methods' Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting (LGB) models were applied for the classification of factors effective in fire. The best result was given by the RF model with 0.93 accuracy, 0.062 F1 score, and 0.753 Area Under Curve (AUC) value. In the classification of factors in the RF model, the grouping between fire-initiating factors and factors effective in spreading is evident, while this distinction is partially noticeable in the other two models.
Keywords
fire risk; wildland-urban interface; rural-urban interface; machine learning classification; GIS
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
