A NEW APPROACH TO DETERMINE THE INFLUENCE OF WEATHER CONDITIONS ON FOREST FIRE RISK IN THE MEDITERRANEAN REGION OF TÜRKİYE

Abstract

The risk of forest fires is a major problem in Türkiye's Mediterranean region and has a significant impact on ecosystems and atmospheric conditions. Throughout the previous century, a significant portion of Türkiye's Mediterranean Region has been destroyed by forest fires. This study aims to determine the meteorological covariates, such as relative humidity, maximum temperature, and wind speed, that affect forest fires. We classified forest fires into two groups. The first group (F1) refers to small forest fires, with burned forest areas of less than 10 hectares. The second group (F2), representing rare events, corresponds to burned areas of more than 10 hectares. The data is composed of binary values (F1=0 and F2=1) taken between the years 2015-2019 from different locations in the Mediterranean Region of Türkiye. For binary data modeling, the ordinary logistic regression (LR) has been frequently used. However, such a method tends to give biased results when using rare event data. Therefore, we employed three different modeling techniques specifically designed for rare event data. According to the results obtained from the best model, Firth's Logistic Regression (FLR), wind speed, and maximum temperature are found to be statistically significant variables in the occurrence of forest fires greater than 10 hectares.

Keywords

• Climate Change
• Humidity
• Natural Hazard
• Penalized Regression

TÜRKİYE'NIN AKDENIZ BÖLGESINDE HAVA KOŞULLARININ ORMAN YANGIN RISKINE ETKISINI BELIRLEMEYE YÖNELIK YENI BIR YAKLAŞIM

Abstract

Orman yangınları riski, Türkiye'nin Akdeniz bölgesinde önemli bir sorundur ve ekosistemlere ile atmosferik özelliklere büyük etkisi vardır. Geçmiş yüzyıl boyunca Türkiye'nin Akdeniz Bölgesi'nin büyük bir kısmı orman yangınları sonucunda tahrip olmuştur. Bu çalışma, orman yangınlarını etkileyen bağıl nem, maksimum sıcaklık ve rüzgâr hızı gibi meteorolojik değişkenleri belirlemeyi amaçlamaktadır. Orman yangınlarını iki gruba ayırdık. Birinci grup (F1), 10 hektardan küçük yanmış orman alanlarını ifade eder. Nadir olayları temsil eden ikinci grup (F2), 10 hektardan fazla yanmış alanları temsil eder. Veriler, Türkiye'nin Akdeniz Bölgesi'ndeki farklı lokasyonlardan 2015-2019 yılları arasında alınan ikili değerlerden oluşmaktadır (F1=0 ve F2=1). İkili veri modellemesi için, genellikle sıradan lojistik regresyon (LR) kullanılmaktadır. Ancak, nadir olay verileri kullanıldığında bu yöntem yanlı sonuçlar verebilmektedir. Bu nedenle, nadir olay verileri için kullanılabilen üç farklı modelleme tekniği kullanıldı. En iyi model olan Firth Lojistik Regresyonu (FLR) sonuçlarına göre, rüzgâr hızı ve maksimum sıcaklık, 10 hektardan büyük orman yangınlarının oluşumunda istatistiksel olarak önemli değişkenler olarak bulundu.

Keywords

• İklim Değişikliği
• Nem
• Doğal Tehlike
• Cezalandırılmış Regresyon

References

1. Chandler, C., Cheney, P., Thomas, P., Trabaud, L. and Williams, D., Fire in Forestry: Vol. II. Forest Fire Management and Organization, Krieger Publishing Company: Malabar, FL, 1991.
2. Schroeder, M.J. and Buck, C.C., Fire Weather: A Guide for Application of Meteorological Information to Forest Fire Control Operations, No. 360, US Forest Service, 1970.
3. Burgan, R.E. and Rothermel, R.C., BEHAVE: fire behavior prediction and fuel modeling system-FUEL subsystem. General Technical Report INT-167. Ogden, UT: U. S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, 1984.
4. Çanakçıoğlu, H., Orman Koruma, İ. Ü. Orman Fakültesi Yayın No: 411/3524, ISBN 975-404-199-7, Istanbul, 1993.
5. Masinda, M.M., Li, F. and Qi, L., "Forest fire risk estimation in a typical temperate forest in Northeastern China using the Canadian forest fire weather index: case study in autumn 2019 and 2020", Natural Hazards, 111(1), 1085-1101, 2002.
6. Holsten, A., Dominic, A.R., Costa, L. and Kropp, J.P., "Evaluation of the performance of meteorological forest fire indices for German federal states", Forest Ecology and Management, 287, 123–131, 2013.
7. Pourtaghi, Z.S., Pourghasemi, H.R., Aretano, R. and Semeraro, T., "Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques", Ecological Indicators, 64, 72–84, 2016.
8. Shirazi, Z., Huadong, G., Fang, C., Bo, Y. and Bin, L., "Assessing the impact of climatic parameters and their inter-annual seasonal variability on fire activity using time series satellite products in South China (2001–2014)", Natural Hazards, 85(3), 1393-1416, 2017.

9. Chowdhury, E.H. and Hassan, Q.K., "Operational perspective of remote sensing-based forest fire danger forecasting systems", Remote Sensing, 104, 224-236, 2015.
10. Koutsias, N., Xanthopoulos, G., Founda, D., Xystrakis, F., Nioti, F., Pleniou, M. and Mallinis, G., "On the relationships between forest fires and weather conditions in Greece from long-term national observations (1894-2010)", International Journal of Wildland Fire, 22(4), 493-507, 2013.
11. Tosic, I., Mladjan, D., Gavrilov, M.B., Zivanović, S., Radaković, M.G., Putniković, S., Petrović, P., Mistridželović, I.K. and Marković, S.B., "Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017", Open Geosciences, 11(1), 414-425, 2019.
12. Stephanie, E.M., Andrea, E.T., Ellis, Q.M., Larissa, L.Y., Jesse, D.Y. and Jose, M.I., "Climate relationships with increasing wildfire in the southwestern US from 1984 to 2015", Forest Ecology and Management, 460, 117861, 2020.
13. Wang, S., Li, H. and Niu, S., "Empirical Research on Climate Warming Risks for Forest Fires: A Case Study of Grade I Forest Fire Danger Zone, Sichuan Province, China", Sustainability, 13, 7773, 2021.
14. Oncel Cekim, H., Güney, C.O., Şentürk, Ö. and Özkan, K., "A novel approach for predicting burned forest area", Natural Hazards, 105, 2187–2201, 2021.
15. Westerling, A.L., Hidalgo, D.R., Cayan, T.W. and Swetnam, T.W., "Warming and earlier spring increase western US forest wildfire activity", Science, 313(5789), 940– 943., 2006.
16. Shikhov, A.N., Perminova, E.S. and Perminov, S.I., "Satellite-based analysis of the spatial patterns of fire-and storm-related forest disturbances in the Ural region, Russia", Natural Hazards, 97, 283–308, 2019.
17. Nolan, R.H., Boer, M.M., Collins, L., Resco De Dios, V., Clarke, H., Jenkins, M., Kenny, B. and Bradstock, R.A., "Causes and consequences of eastern Australia’s 2019–20 season of mega-fires", Global Change Biology, 26,1039–1041, 2020.
18. Tedim, F., Leone, V., Amraoui, M., Bouillon, C., Coughlan, M., Delogu, G., Fernandes, P., Ferreira, C., McCaffrey, S. and McGee, T., "Defining extreme wildfire events: difficulties, challenges, and impacts", Fire, 1(1), 9, 2018.
19. Ozbayoğlu, A.M. and Bozer, R., "Estimation of the burned area in forest fires using computational intelligence techniques", Procedia Computer Science, 12, 282-287, 2012.
20. Beckage, B., Platt, W. and Panko, B., "A climate-based approach to the restoration of fire-dependent ecosystems", Restoration Ecology, 13(3), 429-431, 2005.
21. Iliadis, L.S., "A decision support system applying an integrated fuzzy model for long-term forest fire risk estimation", Environmental Modelling and Software, 20(5), 613-621, 2005.
22. Cortez, P. and Morais, A.D.J.R., "A data mining approach to predict forest fires using meteorological data. In New Trends in Artificial Intelligence", Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimarães, Portugal 25, 512-523, 2007.
23. Cheng, T. and Wang, J., "Integrated spatio‐temporal data mining for forest fire prediction", Trans GIS, 12(5), 591-611, 2008.
24. Sakr, G.E., Elhajj, I.H. and Mitri, G., "Efficient forest fire occurrence prediction for developing countries using two weather parameters", Engineering Applications of Artificial Intelligence, 24(5), 888-894, 2011.
25. Vasconcelos, M.J.P., Silva, S., Tome´, M., Alvim, M. and Pereira, J.M.C., "Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks", Photogrammetric Engineering and Remote Sensing, 67, 73–87, 2001.
26. Syphard, A.D., Radeloff, V.C., Keuler, N.S., Taylor, R.S., Hawbaker, T.J., Stewart, S.I. and Clayton, M.K., "Predicting spatial patterns of fire on a southern California landscape", International Journal of Wildland Fire, 17, 602–613, 2008.
27. Lozano, F.J., Sua´rez-Seoane, S., Kelly, M. and Luis, E., "A multiscale approach for modeling fire occurrence probability using satellite data and classification trees: a case study in a mountainous Mediterranean region", Remote Sensing of Environment, 112, 708–719, 2008.
28. Padilla, M. and Vega-Garcia, C., "On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain", International Journal of Wildland Fire, 20, 46–58, 2011.
29. Del Hoyo, V.L., Martın, I.M. and Vega, M.F., "Logistic regression models for human-caused wildfire risk estimation: Analysing the effect of the spatial accuracy in fire occurrence data", The European Research Journal, 130, 983–996, 2011.
30. Magnussen, S. and Taylor, S.W., "Prediction of daily lightning-and human-caused fires in British Columbia", International Journal of Wildland Fire, 21(4), 342-356, 2012.
31. Firth, D., "Bias reduction of maximum likelihood estimates", Biometrika, 80, 27–38, 1993.
32. Heinze, G. and Schemper, M., "A solution to the problem of separation in logistic regression", Statistics in Medicine, 21, 2409–2419, 2002.
33. King, G. and Zeng, L., "Logistic regression in rare events data", Political Analysis, 9(2), 137-163, 2001.
34. Pesantez-Narvaez, J., Guillen, M. and Alcañiz, M., "RiskLogitboost Regression for Rare Events in Binary Response: An Econometric Approach", Mathematics, 9(5), 579, 2021.
35. Chen, F., Peng, H., Chan, P.W., Ma, X. and Zeng, X., "Assessing the risk of windshear occurrence at HKIA using rare‐event logistic regression", Meteorological Applications, 27 (6), e1962 doi.org/10.1002/met.1962., 2020.
36. Puhr, R., Heinze, G., Nold, M., Lusa, L. and Geroldinger, A., "Firth’s logistic regression with rare events: Accurate effect estimates and predictions?", Statistics in Medicine, 36, 2302–2317, 2017.
37. Olmuş, H., Nazman, E. and Erbaş, S., "Comparison of penalized logistic regression models for rare event case", Communications in Statistics - Simulation and Computation, 51(4), 1578-1590, 2022.
38. Amatulli G., Camia, G.A. and San-Miguel-Ayanz, J., "Estimating future burned areas under changing climate in the EU-mediterranean countries", Science of The Total Environment, 450, 209–222, 2013.
39. Ertuğrul, M., "Orman Yangınlarının Dünyadaki ve Türkiye’deki Durumu", Bartın Orman Fakültesi Dergisi, 7, 43–50, 2005.
40. Turco, M., Llasat, M.C., Von Hardenberg, J. and Provenzale, A., "Climate change impacts on wildfires in a Mediterranean environment", Climatic Change, 125(3), 369-380, 2014.
41. Güney, C.O., Özkan, K. and Şentürk, Ö., "Modelling of spatial prediction of fire ignition risk in the Antalya-Manavgat district", Journal of the Faculty of Forestry Istanbul University, 66, 459–470, 2016.
42. Antalya Regional Directorate of Forestry Retrieved February 03, 2022 from https://antalyaobm.ogm.gov.tr/SitePages/OGM/OGMDefault.aspx,
43. Turkish State Meteorological Service 1930–2019 meteorological statistics. Turkish State Meteorological Service. Ankara, Türkiye Retrieved February 03, 2022, from https://www.mgm.gov.tr/veridegerlendirme/il-ve-ilceleristatistik.aspx?k=A&m=ANTALYA
44. McCuttchan, M.H., Climatic Features as a fire determinant. USDA Forest Service General Technical Report WO, 1977.
45. Güney, C.O., Ryan, K.C., Guney, A. and Hood, S.M., "Wildfire in Türkiye: Fire management challenges at an ancient crossroads of nature and culture", Wildfire, 28(3), 21-28, 2019.
46. 2021 Turkey wildfires, Retrieved December 21, 2022 from https://en.wikipedia.org/wiki/2021_Türkiye_wildfires,