Classification of Forest Fires in European Countries by Clustering Analysis Techniques

Year 2023, , 987 - 1001, 18.10.2023

Hakan Serin , Muslu Kazım Körez , Mehmet Emin Tekin , Sinan Siren

https://doi.org/10.16984/saufenbilder.1288073

Abstract

The biggest threat to the forests, which are natural habitats in European countries, as they are in the whole world, is forest fires. The aim of this study is to group the 38 European countries which have completely accessible fire indexes between the years 2008 to 2022; with respect to their similarities in fire regimes; and to compare the obtained groups with respect to their fire indexes. The clustering technique, which is a data mining method, was used while making these comparisons since it would be more objective and realistic to group and evaluate the countries according to their similarities. In the K-Means technique 2 clusters, and in the Ward's method 3 clusters were obtained. In the K-Means technique, significant statistical differences were found between the 2 clusters in terms of all fire indexes (p<0.05). In the Ward’s method, statistically significant differences were found between the clusters in terms of the number of fires, total area burned (ha) and woodland (p<0.05). In the result of the studies, the fire regimes in Turkey, Bosnia and Herzegovina, Ukraine, Italy, Spain, and Portugal resulted higher than the other countries in both clustering algorithms. Since many factors were taken into consideration in the study, countries heavily associated with fires such as Greece and France were separated from those with high fire regimes. It is recommended to conduct modelling studies with data mining algorithms by taking different fire indexes into account in order to increase the reliability of the results.

Keywords

Data mining method, ward method, k-means, cluster analysis, forest fire

Supporting Institution

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Project Number

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