Efficiency of gene expression programming in diameter-height modeling of Iranian oak (Quercus brantii Lindl).

Document Type : Complete scientific research article

Authors

1 PhD student in Forest Management, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran.

2 Associate Professor, Department of Forestry, Faculty of Natural Resources, Lorestan University, Khorramabad, Iran.

Abstract

Background and purpose: measuring the height of all forest trees is a time-consuming and expensive operation, hence the use of diameter and height models to estimate the height of trees has been developed. The aim of this research is to investigate the efficiency of gene expression programming in diameter-height modeling of Iranian oak species (Quercus brantii Lindl) in high forests of Middle Zagros.
Materials and methods: In order to carry out this research, by conducting numerous forest walk and getting to know the forests of the region, a stand with an area of approximately 5 hectares with a high forests vegetation structure was selected in the protected area of Sefid Koh Lorestan became. In this stand, the characteristics of DBH and total height of all Iranian oak trees whose DBH was more than 12.5 cm were counted 100%. A total of 642 trees were measured. In this research, 80% of the data was used for modeling and 20% for validation. The gene expression model with 3 genes and 100 chromosomes was implemented to investigate the relationship between height as a dependent variable and diameter as an independent variable. In order to evaluate the performance of the final model, RMSE, MAE and R2 criteria were used.
Result:The model extracted from GEP justified 87% of the tree height based on the R2 value. The results of tree height-diameter modeling showed that the final model obtained has coefficient of explanation (R2), root mean square error (RMSE) and mean absolute value of error (MAE) 0.87, 1.3 and 0.97 respectively. is Also, the results of the criteria used to validate the obtained model showed that the extracted model has the coefficient of explanation (R2), root mean square error (RMSE) and mean absolute value of error (MAE), respectively 0.82 and 40. 1 and 1.06 could predict the height of trees.
Conclusion: Overall, the results of this research showed that the model extracted from gene expression programming according to the R2, RMSE and MAE performance evaluation criteria has the ability to estimate the height of Iranian oak high forests in the middle Zagros vegetation zone. Therefore, this model can be used in the forest areas of the middle Zagros vegetation zone, which have the same structure and habitat conditions as the studied area. It should be noted that the present research only predicted the height of trees based on the DBH (independent variable), so it is suggested that in future researches, the generalized models of height and diameter, in which the variability of the habitat and stand Considering the stand variables other than tree diameter (basal area of stand, stand age, dominant height, dominant diameter, site index, etc.) are considered to be used. Also, in order to make a more accurate judgment about the performance of this program, it is better to compare and measure it with other estimation algorithms such as non-linear regressions, support vector machine, artificial neural network, etc. in future studies.

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References

1.Najafifar, A., Sagheb-Talebi, K., & Saeb, K. (2012). The role of light intensity on survival of Quercus branti saplings in relation to slope aspect and distance from seed trees in Ilam province forests. Journal of Forest and Wood Products.
64 (4), 1-14.
2.Zeynali Yadegari, L., & Seyedi, N. (2019). Effect of altitude on seed germination and biomass of Quercus brantii. Journal of Forest Research and Development. 5 (3), 405-417.
3.Rahimi, GH., Mohammadi Samani, K., Shabanian, N., & Shfie Rahmani, M. (2020). Investigation of some chemical properties of soil in two glazed and less disturbed forest stands in North Zagros (Case study: forests of Baneh basin, Kurdistan province). Environmental Science and Technology. 22 (3), 68-55.
4.Salmani, A., Poursaeed, A. R., Bayramzadeh, V., & Eshraghi Samani, R. (2021). Explaining the criteria and indicators of sustainable management of forests in the Zagros basin from the point of view of forest specialists and experts. Iranian Journal of Forest. 13 (1), 43-58.
5.Fattahi, M. (1994). Investigation of Zagros oak forests and the most important factors of its destruction. Research Institute of Forests and Rangelands. Tehran press.
6.Bourque, C. P. A., Bayat, M., & Zhang, C. (2019). An assessment of height–diameter growth variation in an unmanaged Fagus orientalis-dominated forest. European Journal of Forest Research. 138, 607-621.
7.Sirkia, S., Heinonen, J., Miina, J., & Eerikäinen, K. (2015). Subject-specific prediction using a nonlinear mixed model: Consequences of different approaches. Forest Science. 61, 205-212.
8.Ozçelik, R., Diamantopoulou, M. J., Crecente-Campo, F., & Eler, U. (2013). Estimating crimean juniper tree height using nonlinear regression and artificial neural network models. Forest Ecology and Management. 306, 52-60.
9.Zhou, R., Wu, D., Fang, L., Xu, A., & Lou, X. (2018). A Levenberg–Marquardt backpropagation neural Network for predicting forest growing stock based on the least-squares equation fitting parameters. Forests. 9, 757.
10.Bayat, M., Hasani, M., & Heidari Masteali, S. (2020). Ten-year estimation of Fagus orientalis Lipsky increment using artificial neural networks model and multiple linear regression Ramsar Forests. Journal of Forest Research and Development. 6 (3), 381-394.
11.Golob, Ch., Ritter, T., Vosptnic, S., Wassermann, C., & Nuthtroft, A. (2018). A flexible height–diameter model for tree height imputation on forest inventory sample plots using repeated measures from the past. Journal Forests. 9 (368), 1-25.
12.Hamidi, S. K., Fallah, A., Bayat, M., & Hosseini Yekani, S. A. (2021). Investigating the diameter and height models of beech trees in uneven age forest of northern Iran (Case study: Farim Forest). Ecology of Iranian Forests, 9 (17), 30-40.
13.Ahmadi, K., Alavi, S. J., Kouchaksaraei, M. T., & Aertsen, W. (2013). Non-linear height-diameter models for oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forests, Iran. Biotechnology. Agronomy, Society, and Environment. 17, 431-440.
14.Alemi, A., Oladi, J., Fallah, A., & Maghsodi, Y. (2021). Evaluating different height-diameter nonlinear models for hornbeams in uneven-aged stands (Case study: Golestan Rezaeian Forest). Ecology of Iranian Forests. 8 (16), 29-38.
15.Bolat, F., Urker, O., & Günlü, A. (2022). Nonlinear height-diameter models for Hungarian oak (Quercus frainetto Ten.) in Dumanlı Forest Planning Unit, Anakkale/Turkey. Austrian Journal of Forest Science. 139, 199-220.
16.Wang, T. Y., & Lam, T. Y. (2021). Modeling the height-diameter relationship of fifteen tree species planted on reclaimed agricultural lands with random species effects. Tropical Forestry. 1053, 1-5.
17.Tabassum, A., Jeelani. M. L., & Sharma, M. (2023). Predictive Modelling of height and diameter relationships of Himalayan chir Pine. Agricultural Science Digest. 43 (2), 170-175.
18.Ercanli, I. (2020). Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height. Forest Ecosystems. 7 (12), 2-18.
19.Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems. 13 (2), 87-129.
20.Hoseinian, F. S., Faradonbeh, R. S., Abdolahzadeh, A., Rezai, B., & Soltani-Mohammadi, S. (2017). Semi-autogenous mill power model development using gene expression programming. Powder Technology. 308, 61-69.
21.Delpasand, S., Maleknia, R., & Kazemi, Y. (2017). Evaluating the impact of climatic factors on vegetation changes in the protected area of Sefid Koh Lorestan using the MODIS sensor. Conference: National Geomatics Conference. pp. 1-10.
22.Zakaria, N. A., Azamathulla, H. M., Chang, C. K., & Ghani, A. A. (2010). Gene expression programming for total bed material loud estimation-a case study. Science of the Total Environment. 408 (21), 5078-5085.
23.Amiri, P., Soosani, J., & Naghavi, H. (2024). Investigating diameter-height models of Persian oak (Quercus brantii Lindl.) in height forests of Middle Zagros. Forest Research and Development. 10 (1), 19-38.
24.Bihamta, M. R., & Zare Chahouki, M. R. (2008). Principles of statistics for the natural resource. University of Tehran Press. 322p.
25.Gonzalez, M. S., Canellas, I., & Montero, G. (2007). Generalized height-diameter and crown diameter prediction models for cork oak forests in Spain. Forest Systems. 16 (1), 76-88.
26.Ahmadi, K., & Alavi, S. J. (2016). Generalized height-diameter models for Fagus orientalis Lipsky in Kyrcanian forest, Iran. Journal of Forest Science. 62 (9), 413-421.
27.Dey, T., Ahmed, Sh., & Islam, M. D. A. (2021). Relationships of tree height-diameter at breast height (DBH) and crown diameter-DBH of Acacia auriculiformis plantation. Asian Journal of Forestry. 5 (2), 71-75.
28.Tuan, N. T., Dinh, T. T., & Long, Sh. H. (2019). Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models. Journal of Forest Science.
65 (4), 134-143.
29.Adame, P., Del Rio, M., & Canellas, I. (2008). A mixed nonlinear height–diameter model for Pyrenean oak (Quercus pyrenaica Willd.). Forest Ecology and Management. 256, 88-98.
30.Sharma, R. P., Vacek, Z., & Vacek, S. (2016). Nonlinear mixed effect height-diameter model for mixed species forests in the central part of the Czech Republic. Journal of Forest Science.
62 (10), 470-484.
31.Ghaderi, P., Mohammadi, J., Shataee, Sh., Rahmani, R., & Kariminejad, N. (2023). The efficiency of nonlinear mixed-effects model in determining height-diameter equations of velvet maple and ironwood trees. Iranian Journal of Forest. 14 (4), 473-485.
32.Ebrahimi, F., Nakhai, M., Naseri, H. R., & Khodai, K. (2021). Estimation of LNAPL height in oil-contaminated aquifers using GEP, ANFIS, MLR. Iranian Geology Quarterly. 15 (57), 29-43.
Journal of Wood and Forest Science and Technology
Volume 31, Issue 4
February 2025
Pages 1-19

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