Clustering of forest road construction and maintenance costs of Regional Forest Directorates in Türkiye using k-medoid clustering method

Öz

Background and aims Forest roads are important infrastructures for wood harvesting, silvicultural interventions etc. In this context, in order to carry out these activities continuously, both the forest roads construction and the maintenance of constructed forest roads are necessary in certain periods. These road construction and maintenance activities are high-cost activities. In this context, due to different factors (topographic conditions, climatic conditions, wood harvesting, etc.), the costs of forest road construction and forest road maintenance activities are may vary regionally. The aim of this study is to cluster the forest regional directorates by considering the average cost values of forest road construction and forest road maintenance activities carried out in 28 forest regional directorates in Türkiye between 2015 and 2023. Methods In the study, the k-medoid clustering method was used as the clustering analysis method. Results It was determined that the average forest road construction cost in Turkey between 2015-2023 was 5,223.13$/km, and the forest road maintenance cost was 73.96 $/km. According to the results, it was found that the regional forest directorates were divided into five clusters in terms of costs and showed a regionally different distribution, and the average of three clusters was above the Turkey average in terms of both forest road construction and forest road maintenance variables. It also has been determined that the forest regional directorates in these clusters were mainly located in the Black Sea and Mediterranean regions. Conclusion The method used in the study has enabled the spatial evaluation of the relevant forest regional directorates in terms of forest road construction and maintenance costs. In this context, the results obtained will contribute to the correct spatial distribution of the budgets created for the relevant activities in the planning phase. In further studies, clustering analyses can be conducted at the provincial, forest enterprise directorate or forest enterprise chief scale and regional cost situations can be more comprehensively revealed.

Anahtar Kelimeler

• Clustering analysis
• wood harvesting
• forest roads
• road maintenance

Türkiye’de Orman Bölge Müdürlüklerinin orman yol yapımı ve bakımı maliyetlerinin k-medoid kümeleme yöntemi ile kümelenmesi

Öz

Giriş ve Hedefler Orman yolları, odun üretimi, silvikültürel müdahaleler gibi aktivitelerin gerçekleştirilmesi için önemli altyapılardır. Bu bağlamda, bu aktivitelerin sürekli olarak yapılabilmesi için belirli dönemler halinde hem orman yollarının yapımı hem de inşaatı yapılan orman yollarının bakımı söz konusu olmaktadır. Söz konusu bu yol yapım ve bakım aktiviteleri yüksek maliyet oluşturan aktivitelerdir. Bu bağlamda, farklı faktörler nedeniyle (topoğrafik koşullar, iklimsel koşullar, odun üretimi vb.) orman yolu yapımı ve orman yol bakımı aktivitelerinin maliyetleri bölgesel olarak değişkenlik gösterebilmektedir. Bu çalışmanın amacı, Türkiye’de 2015-2023 yılları arasında 28 adet orman bölge müdürlüklerinde gerçekleştirilen ortalama orman yolu yapımı ve orman yolu bakımı maliyet değerlerini dikkate alarak, orman bölge müdürlüklerini kümelemektir. Yöntem Çalışmada kümeleme analizi yöntemi olarak k-medoid kümeleme yöntemi kullanılmıştır. Bulgular 2015-2023 yılları arası Türkiye geneli ortalama orman yolu yapım maliyetinin 5.223,13 $/km olduğu, orman yolu bakım maliyetinin ise 73,96 $/km olduğu belirlenmiştir. Elde edilen sonuçlarına göre ise orman bölge müdürlüklerinin maliyetler açısından beş kümeye ayrılarak bölgesel olarak farklı dağılım gösterdiği, üç kümenin ortalamasının hem orman yolu yapım hem de orman yolu bakım değişkeni açısından Türkiye ortalaması üstünde olduğuna ulaşılmıştır. Bu kümelerde bulunan orman bölge müdürlüklerinin ağırlıklı olarak Karadeniz ve Akdeniz bölgelerinde dağılım gösterdiği belirlenmiştir. Sonuçlar Çalışmada kullanılan yöntem, orman yolu yapım ve bakım maliyetleri açsından ilgili orman bölge müdürlüklerinin mekânsal olarak değerlendirilmesine imkân sağlamıştır. Bu bağlamda ulaşılan sonuçlar planlama aşamasında ilgili aktiviteler için oluşturulan bütçelerin mekânsal olarak doğru biçimde dağıtılmasına katkı sağlayacaktır. İlerleyen çalışmalarda, il, orman işletme müdürlüğü veya orman işletme şefliği ölçeğinde kümeleme analizleri yapılıp bölgesel maliyet durumları daha kapsamlı ortaya koyulabilir.

Anahtar Kelimeler

• Kümeleme analizi
• odun üretimi
• orman yolları
• yol bakımı

Kaynakça

1. Abdi, E., Majnounian, B., Darvishsefat, A., Mashayekhi, Z., Sessions, J., 2009. A GIS-MCE based model for forest road planning. Journal of Forest Science 55, 171–176.
2. Abeli, W.S., Shemwetta, D.T.K., Meiludie, R.E.L.O., Kachwele, M., 2000. Road alignment and gradient ıssues in the maintenance of logging roads in Tanzania. Journal of Forest Engineering 11, 15–21.
3. Akay, A.O., Akgül, M., Esin, A., Demir, M., Şentürk, N., Öztürk, T., 2021. Evaluation of occupational accidents in forestry in Europe and Turkey by k-means clustering analysis. Turkish Journal of Agriculture and Forestry 45, 495–509.
4. Akgul, M., Akay, A.O., Ozocak, M., Esin, A.İ., Şenturk, N., 2022. A new approach to spatial risk analysis in the long-term (1950–2020) assessment of natural disasters (avalanche, landslide, rockfall, flood) in Turkey. Natural Hazards 114, 3471–3508.
5. Dražić, S., Danilović, M., Ristić, R., Stojnić, D., Antonić, S., 2023. Evaluation of morphometric terrain parameters and their influence on determining optimal density of primary forest road network. Croatian Journal of Forest Engineering : Journal for Theory and Application of Forestry Engineering 44, 301–312.
6. Dsouza, S., Dsouza, J. D., Vanitha, T., 2017. Analysis of data using k-means and k-medoids algorithms. International Journal of Latest Trends in Engineering and Technology Special Issue, 370-373.
7. Elibüyük, M., Yılmaz, E., 2010. Türkiye’nin coğrafi bölge ve bölümlerine göre yükselti basamakları ve eğim grupları. Coğrafi Bilimler Dergisi 8, 27–56.
8. Fan, C., Xiao, F., Li, Z., Wang, J., 2018. Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review. Energy and Buildings 159, 296–308.

9. Faria, F.N. de, da Silva Lopes, E., Sampietro, J.A., Correia, R.J., 2024. Forest road density in flat and sloping site conditions in Brazil. International Journal of Forest Engineering 35, 84–92.
10. Ghajar, I., Najafi, A., Karimimajd, A.M., Boston, K., Ali Torabi, S., 2013. A program for cost estimation of forest road construction using engineer’s method. Forest Science and Technology 9, 111–117.
11. Jaafari, A., Pazhouhan, I., Bettinger, P., 2021. Machine learning modeling of forest road construction costs. Forests 12, 1169.
12. Jain, A.K., 2010. Data clustering: 50 years beyond K-means. Pattern Recognition Letters, Award Winning Papers from the 19th International Conference on Pattern Recognition (ICPR) 31, 651–666.
13. Jin, H., Chen, X., Wu, P., Song, C., Xia, W., 2021. Evaluation of spatial-temporal distribution of precipitation in mainland China by statistic and clustering methods. Atmospheric Research 262, 105772.
14. Kaufman, L., Rousseeuw, P., 1990. Finding Groups in Data: An Introduction To Cluster Analysis, Wiley, New York. ISBN 0-471-87876-6.
15. Krumov, T., 2019. Determination of the optimal density of the forest road network. Journal of Forest Science 65, 438–444.
16. Martino, A., Rizzi, A., Frattale Mascioli, F.M., 2019. Efficient Approaches for Solving the Large-Scale k-Medoids Problem: Towards Structured Data. In: Sabourin, C., Merelo, J.J., Madani, K., Warwick, K. (Eds.), Computational Intelligence. Springer International Publishing, Cham, 199–219.
17. MGM, 2024 Meteoroloji Genel Müdürlüğü (MGM), Coğrafi bölgelerin yağış değerleri https://www.mgm.gov.tr/FILES/arastirma/yagis-degerlendirme/2023yagisdegerlendirmesi.pdf (Erişim Tarihi 13.06.2024).
18. Motlagh, A.R., Parsakhoo, A., Najafi, A., Mohammadi, J., 2024. Development of a Sustainable Maintenance Strategy for Forest Road Wearing Courses in Different Climate Zones. Croatian Journal of Forest Engineering : Journal for Theory and Application of Forestry Engineering 45, 139–156.
19. Najafi, A., Richards, E.W., 2013. Designing a Forest Road Network Using Mixed Integer Programming. Croatian Journal of Forest Engineering : Journal for Theory and Application of Forestry Engineering 34, 17–30.
20. OGM, 2024a Orman Genel Müdürlüğü (OGM), yıllık faaliyet raporları https://www.ogm.gov.tr/tr/faaliyet-raporu (Erişim Tarihi 13.06.2024).
21. OGM, 2024b Orman Genel Müdürlüğü (OGM), Üretim, Satış ve Stok Faaliyetleri https://www.ogm.gov.tr/tr/e-kutuphane-sitesi/Pages/UretimSatisveStokFaaliyetleri.aspx (Erişim Tarihi 13.06.2024).
22. Pazhouhan, I., Najafi, A., Rouhani, A.K., Vahidi, J., 2017. Effect of subsurface materials on earthwork operation costs of forest road. European Journal of Forest Engineering 3, 44–51.
23. Picchio, R., Pignatti, G., Marchi, E., Latterini, F., Benanchi, M., Foderi, C., Venanzi, R., Verani, S., 2018. The Application of Two Approaches Using GIS Technology Implementation in Forest Road Network Planning in an Italian Mountain Setting. Forests 9, 277.
24. Rodrigues, D., Pinho-Lopes, M., Macedo, J., 2024. Classification Systems Applied to Forest Road Planning: Research Gap Analysis. Forests 15, 968.
25. Shang, Q., Yu, Y., Xie, T., 2022. A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering. Sustainability 14, 11068.
26. Sobrinho Campolina Martins, A., Ramos de Araujo, L., Rosana Ribeiro Penido, D., 2024. K-Medoids clustering applications for high-dimensionality multiphase probabilistic power flow. International Journal of Electrical Power & Energy Systems 157, 109861.
27. Stückelberger, J.A., Heinimann, H.R., Burlet, E.C., 2006. Modeling spatial variability in the life-cycle costs of low-volume forest roads. European Journal of Forest Research 125, 377–390.
28. Sureja, N., Chawda, B., Vasant, A., 2022. An improved K-medoids clustering approach based on the crow search algorithm. Journal of Computational Mathematics and Data Science 3, 100034.
29. Tampekis, S., Samara, F., Sakellariou, S., Sfougaris, A., Christopoulou, O., 2018. An eco-efficient and economical optimum evaluation technique for the forest road networks: the case of the mountainous forest of Metsovo, Greece. Environmental Monitoring and Assessment 190, 134.
30. Tibshirani, R., Walther, G., Hastie, T., 2001. Estimating the Number of Clusters in a Data Set Via the Gap Statistic. Journal of the Royal Statistical Society Series B: Statistical Methodology 63, 411–423.
31. TCMB, 2024 Türkiye Cumhuriyet Merkez Bankası (TCMB) Dolar fiyatları https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket/#collapse_2 (Erişim Tarihi 13.06.2024).
32. Velmurugan, 2010. Computational complexity between K-means and K-medoids clustering algorithms for normal and uniform distributions of data points. Journal of Computer Science 6, 363–368.
33. Wang, S., Liu, H., Pu, H., Yang, H., 2020. Spatial disparity and hierarchical cluster analysis of final energy consumption in China. Energy 197, 117195.
34. Whasphuttisit, J., Jitsakul, W., Kaewkiriya, T., 2022. Comparison of Clustering Techniques for Thai Mutual Funds Fee Dataset. 2022 14th International Conference on Knowledge and Smart Technology (KST). Presented at the 2022 14th International Conference on Knowledge and Smart Technology (KST), 125–130.
35. Yao, Z., Kim, C., 2022. Analyzing the multiscale patterns of jobs-housing balance and employment self-containment by different income groups using LEHD data: A case study in Cincinnati metropolitan area. Computers, Environment and Urban Systems 96, 101851.
36. Yousefi, S., Emami, S. N., Nekoeimehr, M., Rahmati, O., Imaizumi, F., Gomez, C., Valjarevic, A. 2024. A hot-spot analysis of forest roads based on soil erosion and sediment production. Land, 13, 1583.
37. Zhen, X., Wang, R., Han, H., Wang, S., Wang, Z., Li, X., 2023. The expansion plan for charging stations based on K-medoids and vehicle GPS data. 11th International Conference on Information, Communication and Networks, pp. 19-23.