Simulation of over-bark tree bole diameters, through the RFr (Random Forest Regression) algorithm
References
- Algera, M., Kätsch, C., Chirwa, P.W., 2019. Developing a taper model for the Pinus elliottii x P. caribaea var. hondurensis hybrid in South Africa. Southern Forests: a Journal of Forest Science, 81: 141–150. https://doi.org/10.2989/20702620.2018.1520027
- Arkes, J., 2019. Regression analysis. A practical introduction. London: Routledge. 362 p.10.4324/9781351011099
- Avery, T.E., Burkhart, H.E., 2002. Forest measurements. New York, USA: Mc Graw Hill. 456 p.
- Barna, M., Ferezliev, A., Tsakov, H., Mihál, I., 2020. Investigations of mature Scots pine stands in windthrow areas in Norway spruce forests in Western Rhodopes. Folia Oecologica, 47: 1–9. doi: 10.2478/foecol-2020-0001
- Bayat, M., Bettinger, P., Heidari, S., Henareh Khalyani, A., Jourgholami, M., Hamidi, S.K, 2020. Estimation of tree heights in an uneven-aged, mixed forest in northern Iran using artificial intelligence and empirical models. Forests, 11 (3): 324. https://doi.org/10.3390/f11030324
- Bi, H., Hamilton, F., 1998. Stem volume equations for native tree species in southern New South Wales and Victoria. Australian Forestry, 61 (4): 275–286. https://doi.org/10.1080/00049158.1998.10674752
- Boroughani, M., Soltani, S., Ghezelseflu, N., Pazhouhan, I., 2022. A comparative assessment between artificial neural network, neuro-fuzzy, and support vector machine models in splash erosion modelling under simulation circumstances. Folia Oecologica, 49 (1): 23–34. https://doi.org/10.2478/foecol-2022-0003
- Breiman, L., 2001. Random forests. Machine Learning, 45: 5–32. http://dx.doi.org/10.1023/A:101093340432410.1023/A:1010933404324
- Cutler, A., Cutler, D.R., Stevens, J.R., 2012. Random forests. In Zhang, C., Ma, Y.Q. (eds). Ensemble machine learning. New York: Springer, 2015, p. 157–175. http://dx.doi.org/10.1007/978-1-4419-9326-7_510.1007/978-1-4419-9326-7_5
- Diamantopoulou, M.J., 2005. Artificial neural networks as an alternative tool in pine bark volume estimation. Computers and Electronics in Agriculture, 48: 235–244. https://doi.org/10.1016/j.compag.2005.04.002
- Diamantopoulou, M.J., Milios, E., Doganos, D., Bistinas, I., 2009. Artificial neural network modeling for reforestation design through the dominant trees bolevolume estimation. Natural Resource Modeling, 22: 511–543. https://doi.org/10.1111/j.1939-7445.2009.00051.x
- Diamantopoulou, M.J., Özçelik, R., Yavuz, H., 2018. Treebark volume prediction via machine learning: a case study based on black alder’s tree-bark production. Computers and Electronics in Agriculture, 151: 431–440. https://doi.org/10.1016/j.compag.2018.06.039
- Draper, N.R., Smith, H., 1998. Applied regression analysis. New York: Wiley. 706 p.10.1002/9781118625590
- Furnival, G., 1961. An index for comparing equations used in constructing volume tables. Forest Science, 7: 337–341.
- Güner, S.T., Diamantopoulou, M.J., Poudel, K.P., Çömez, A., Özçelik, R., 2022. Employing artificial neural network for effective biomass prediction: an alternative approach. Computers and Electronics in Agriculture, 192: 106596. https://doi.org/10.1016/j.compag.2021.106596
- Hastie, T., Tibshirani, R., Friedman, J., 2017. The elements of statistical learning data mining, inference, and prediction. 2nd ed. New York: Springer. 764 p.
- Hoaglin, D.C., Mosteller, F., Tukey, J.W., 2006. Exploring data tables, trends and shapes. New York: John Wiley and Sons Inc. 538 p.10.1002/9781118150702
- Hirsch, R.P., 1991. Validation samples. Biometrics, 47: 1193–1194. [cit. 2022-01-09]. http://www.jstor.org/stable/2532672
- IBM SPSS Corp., released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.
- Leite, H.G., Da Silva, M.L.M., Binoti, D.H.B., Fardin, L., Takizawa, F.H., 2011. Estimation of inside-bark diameter and heartwood diameter for Tectona grandis Linn. trees using artificial neural networks. European Journal of Forest Research, 130: 263–269. https://doi.org/10.1007/s10342-010-0427-7
- Lilliefors, H.W., 1967. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown. Journal of the American Statistical Association 62: 399–402. https://doi.org/10.2307/2283970
- Koulelis, P.P., Ioannidis, K., 2021. Constructing single-entry stem volume models for four economically important tree species of Greece. Folia Oecologica, 48 (2): 136–146. https://doi.org/10.2478/foecol-2021-0014
- Marchi, M., Scotti, R., Rinaldini, G., Cantiani, P., 2020. Taper function for Pinus nigra in central Italy: Is a more complex computational system required? Forests, 11: 405. https://doi.org/10.3390/f11040405
- Matis, K.G., 2004. Forest biometry: II. Dendrometry. 2nd ed. Thessaloniki, Greece: Pegasus. 674 p. (In Greek).
- Meng, C.H., Tsai, W.Y., 1986. Selection of weights for a weighted regression of tree volume. Canadian Journal of Forest Research, 16 (3): 671–673. https://doi.org/10.1139/x86-118
- Moore, J.A., Zhang, L., Stuck, D., 1996. Height-diameter equations for ten tree species in the Inland Northwest. Western Journal of Applied Forestry, 11: 132–137. https://doi.org/10.1093/wjaf/11.4.132
- Nanos, N., Montero, G., 2002. Spatial prediction of diameter distribution models. Forest Ecology and Management, 161: 147–158. https://doi.org/10.1016/S0378-1127(01)00498-4
- Nicoletti, M.F., Carvalho, S.D.P.C., Do Amaral Machado, S., Costa, V.J., Silva, C.A., Topanotti, L.R., 2020. Bivariate and generalized models for taper stem representation and assortments production of loblolly pine (Pinus taeda L.). Journal of Environmental Management, 270:110865.https://doi.org/10.1016/j.jenvman.2020.11086532721311
- Nunes, M.H., Görgens, E.B., 2016. Artificial intelligence procedures for tree taper estimation within a complex vegetation mosaic in Brazil. PLoS ONE, 11: e0154738. https://doi.org/10.1371/journal.pone.0154738487149027187074
- Özçelik, R., Karatepe, Y., Gürlevik, N., Cañellas, I., Crecente-Campo, F., 2016. Development of ecoregion-based merchantable volume systems for Pinus brutia Ten. and Pinus nigra Arnold. in southern Turkey. Journal of Forestry Research, 27: 101–117. https://doi.org/10.1007/s11676-015-0147-4
- Özçelik, R., Diamantopoulou, M.J., Trincado, G., 2019. Evaluation of potential modeling approaches for Scots pine stem diameter prediction in north-eastern Turkey. Computers and Electronics in Agriculture, 162: 773–782. https://doi.org/10.1016/j.compag.2019.05.033
- Özçelik, R., Alkan, O., 2020. Fitting and calibrating a mixed-effects segmented taper model. Cerne, 26: 464–473. https://doi.org/10.1590/01047760202026032737
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., 2011. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12: 2825–2830.
- Philip, M.S., 1994. Measuring trees and forests. 2nd ed. Wallingford, UK: CAB International. 310 p.10.1079/9780851988832.0000
- Prasad, A.M., Iverson, L.R., Liaw, A., 2006. Newer classification and regression techniques: bagging and random forests for ecological prediction. Ecosystems, 9: 181–199. https://doi.org/10.1007/s10021-005-0054-1
- Python Software Foundation, 2022. Python 3.9. https://docs.python.org/3.9/index.html
- Ratkowsky, D.A., 1990. Handbook of nonlinear regression models. Statistics: Textbooks and Monographs, vol. 107. New York: Marcel Dekker Inc. 241 p.
- Salekin, S., Catalan, C.H., Boczniewicz, D., Phiri, D., Morgenroth, J., Meason, D.F., Mason, E.G., 2021. Global tree taper modelling: a review of applications, methods, functions, and their parameters. Forests, 12: 913. https://doi.org/10.3390/f12070913
- Segal, M.R., 2003. Machine learning benchmarks and random forest regression. San Francisco: Center for Bioinformatics and Molecular Biostatistics, University of California. [cit.2022-01-09]. https://escholarship.org/uc/item/35x3v9t4
- Soares, F.A.A.M.N., Flôres, E.L., Cabacinha, C.D., Carrijo, G.A., Veiga, A.C.P., 2011. Recursive diameter prediction and volume calculation of Eucalyptus trees using multilayer perceptron networks. Computers and Electronics in Agriculture, 78: 19–27. https://doi.org/10.1016/j.compag.2011.05.008
- Trincado, G., Burchart, H.E., 2006. A generalized approach for modeling and localizing stem profile curves. Forest Science, 52: 670–682.
- Van Laar, A., Akça, A., 2007. Forest mensuration. Dordrecht, The Netherlands: Springer. 383 p.10.1007/978-1-4020-5991-9
- Van Rossum, G., Drake, F.L., 2011. The Python language reference manual. Network Theory Ltd. 150 p.
- West, P.W., 2009. Tree and forest measurement. 2nd ed. Berlin: Springer-Verlag. 191 p.10.1007/978-3-540-95966-3
Language: English
Page range: 93 - 101
Submitted on: Jan 12, 2022
Accepted on: May 20, 2022
Published on: Aug 5, 2022
Published by: Slovak Academy of Sciences, Mathematical Institute
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Related subjects:
© 2022 Maria J. Diamantopoulou, published by Slovak Academy of Sciences, Mathematical Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.