استفاده از روش شبکة عصبی مصنوعی برای پیش‌بینی حجم سرپای درخت صنوبر دلتوئیدس ( Populus deltoids) (مطالعة موردی: جنگل‌های شرکت شفارود)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و صنایع چوب و کاغذ، دانشکدة کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

2 بخش صنایع چوب و کاغذ، معاون اجرایی شرکت شفارود، گیلان، ایران.

3 گروه جنگل، دانشکدۀ کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، اردبیل، ایران.

4 کارشناسی علوم باغبانی، مدیرعامل شرکت شفارود، گیلان، ایران.

10.22059/jfwp.2023.354060.1236

چکیده

با توجه به اهمیت اندازه­ گیری حجم و امکان فروش درختان به شکل سرپا و همچنین هزینه‌بر و زمان­بر بودن اندازه ­گیری حجم واقعی درختان، برای یافتن روشی جایگزین که از لحاظ زمان و هزینه به صرفه‌تر باشد، این مطالعه با هدف پیش­بینی حجم سرپای درختان صنوبر (Populus deltoids) با روش شبکة عصبی مصنوعی (ANN)  برای مدیریت برداشت و فروش، پیش­بینی شد. در این مطالعه قطر و ارتفاع تعداد 416 اصله درخت با انتخاب تصادفی از دامنه­های قطری 10 تا 15، 15 تا 20، 20 تا 25، 25 تا 30، 30 تا 35، 35 تا 40، 40 تا 45 و 45 تا50 (سانتی‌متر) اندازه ­گیری شد. سپس حجم سرپای آن‌ها با مدل تک لایه شبکة عصبی مصنوعی پیش­بینی شد. در این مطالعه شاخص های پیش ­بینی کنندة قطر و ارتفاع برابر سینه به‌عنوان دادة ورودی و حجم سرپای درختان به‌عنوان داده خروجی در نظر گرفته شد. تعداد لایه ­های ورودی، مخفی و خروجی همگی یک و تعداد نورون ­های لایة مخفی بر حسب آزمون و خطا 10 در نظر گرفته شد. نتایج نشان داد که مدل سادة شبکة عصبی مصنوعی با شاخص پیش ­بینی کنندة قطر با MAPE، R2 ،R، MSE، RMSE  به‌ترتیب 10/22، 0/9785، 0/99، 0/072 و 0/269 و با شاخص ارتفاع با MAPE، R2 ،R، MSE، RMSE  به‌ترتیب 35/43، 0/8004، 0/89، 0/674 و 0/821 حجم را پیش ­بینی کرد. مدل ساده به‌دلیل سهولت انجام کار و به‌عنوان بهترین مدل پیشنهاد شدند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Predicting the volume of Populus deltoids using the artificial neural network method (Case study: Shafarud forest company)

نویسندگان [English]

  • Ali Shokrvand Shakiba 1
  • Akbar Rostampour Haftkhani 1
  • Mehdi Kalagar 2
  • Kiomars Sefidi 3
  • Majid Saffari 4
1 Department of Wood and Paper Science, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
2 Department of Wood and Paper Science, Executive manager of Shafarood Company, Gilan, Iran.
3 Department of Forestry, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
4 Expert of Horticultural Sciences, CEO of Shafarood Company, Gilan, Iran.
چکیده [English]

Given the importance of measuring tree volume and the potential for selling standing trees, as well as the time-consuming and costly nature of measuring the actual volume of trees, this study aimed to find an alternative method that is more economical in terms of time and cost for predicting the standing volume of poplars (Populus deltoids) using an artificial neural network (ANN) method for harvesting and sales management. In this study, the diameters and heights of 416 randomly selected trees were measured. The tree diameters ranged from 10 to 15, 15 to 20, 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45, and 45 to 50 centimeters. Their standing volumes were then predicted using both simple and multiple ANN models. In this study, diameter at breast height and height were considered predictor indices for the input data, while the standing volume of the tree was the output data. The number of input, hidden, and output layers was kept uniform at one layer. The number of hidden layer neurons was determined to be 10 using trial and error. The results showed that the simple ANN model using the diameter index yielded MAPE and R-squared values of 10.22 and 0.9785, respectively, while the model using the height index produced MAPE and R-squared values of 35.43 and 0.8004, respectively. Due to the simple model's ability to predict volume with an error of approximately 10% using the diameter predictor, the simple model with the diameter index was suggested as the best model overall, considering its ease of implementation and superior accuracy.

کلیدواژه‌ها [English]

  • Forest exploitation
  • Hand-planted forest
  • Onelayer perceptron
  • Wood cultivation

مراجع

  • Mohebbi Gargari, R., Bayat Kashkoli, A., & Moazami, V. (2018). Survey of effective criteria for sustainable development of poplar wood farming in Iran by pair comparisons method. Iranian Journal of Wood and Paper Industries, 9(2), 49-235.
  • Lashkarbolouki, E, Pourtahmasi, K., Oladi R., Kalagari, R, Alizadeh, H.) 2016.( Recognition and ratting off effecting indexes on the consumption of pulp and paper industry production from different poplar plantation sites in Iran. Iranian Journal of Wood and Paper Industries, 7(3), 36-425.
  • Bombrun, M., Dash, J.P., Pont, D., Watt, M.S., Pearse, G.D., Dungey, H.S. )2020.( Forest-scale phenotyping: Productivity characterisation through machine learning. Frontiers in Plant Science, 11:99.
  • Lhotka, J.M., & Loewenstein, E.F. )2011). An individual-tree diameter growth model for managed uneven-aged oak-shortleaf pine stands in the Ozark highlands of Missouri, USA. Forest Ecology and Management, 261(3):8-770.
  • Sharma, R., 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.
  • Rex, F.E., Silva, C.A., Dalla Corte, A.P., Klauberg, C., Mohan, M., Cardil, A., da Silva, V.S., de Ameida, D.R.A., Garcia, M., Braodbent, E.N., Valbuena R., Stoddart, J., Merrick, T., & Hudak, A.T. (2020). Comparison of statistical modelling approaches for estimating tropical forest aboveground biomass stock and reporting their changes in low-intensity logging areas using multi-temporal LiDAR data. Remote Sensing, 12(9): 1498.
  • Bayat, M., Pukkala, T., Namiranian, M., & Zobeiri, M. (2013). Productivity and optimal management of the uneven-aged hardwood forests of Hyrcania. European Journal of Forest Research, 132(5), 64-851.
  • Ou, Q., Lei, X., & Shen, C. (2019). Individual tree diameter growth models of larch–spruce–fir mixed forests based on machine learning algorithms. Forests, 10(2), 187.
  • Diamantopoulou, M.J. (2005). Predicting fir trees stem diameters using artificial neural network models. Southern African Forestry Journal, 205(1), 44-39.
  • Özçelik, R., Cao, Q.V., Trincado, G., & Göçer, N. (2018). Predicting tree height from tree diameter and dominant height using mixed-effects and quantile regression models for two species in Turkey. Forest Ecology and Management, 419, 8-240.
  • Júnior, I.S.T., de Souza, J.R.M., de Sousa Lopes, L.S., Fardin, L.P., Casas, G.G., de Oliveira Neto, R.R., Leite R.V., Leite H.G. (2021). Machine learning and regression models to predict multiple tree stem volumes for teak. Southern Forests, 83(4), 294-302.
  • Lacerda, T.H.S., Cabacinha, C.D., Araújo, C.A., Maia, R.D., & Lacerda, K.W.S. (2017). Artificial neural networks for estimating tree volume in the Brazilian savanna. Cerne, 23: 483-491.
  • Özçelik, R., Diamantopoulou M.J., Brooks J.R., Wiant Jr H.V. (2010). Estimating tree bole volume using artificial neural network models for four species in Turkey. Journal of Environmental Management, 91(3), 53-742.
  • Karatepe, Y., Diamantopoulou, M.J., Özçelik, R., & Sürücü, Z. (2022). Total tree height predictions via parametric and artificial neural network modeling approaches. iForest-Biogeosciences and Forestry, 15(2):95.
  • Lima, R.B.D., Ferreira, R.L.C., da Silva, J.A.A., Alves Junior, F.T., & de Oliveira, C.P. (2021). Estimating tree volume of dry tropical forest in the Brazilian semi-Arid region: a comparison between regression and artificial neural networks. Journal of Sustainable Forestry, 40(3): 281-299.
  • Bayati, H., & Najafi, A. (2013). Performance comparison artificial neural networks with regression analysis in trees trunk volume estimation. Forest and Wood Products, 66(2): 177-191.
  • Leite, R.V., do Amaral, C.H., Pires, R.P., Silva, C.A., Soares, C.P.B., Macedo, R.P., da Silva, A.A.L., Broadbent, E.N., Mohan, M., & Leite, H.G. (2020). Estimating stem volume in eucalyptus plantations using airborne LiDAR: A comparison of area-and individual tree-based approaches. Remote Sensing, 12(9): 1513.
  • Zobeiri M. (2005). Forest Inventory (Measurement of Tree and Forest).University of Tehran Press, Tehran (In Persian)
  • Jahani, A., Kalagari, M., Modirrahmati, A., & Ghasemi, R. (2014). Determining the best stem form factor equation for populous deltoides in poplar plantations of Alborz Research Station, Karaj. Iranian Journal of Forest and Poplar Research, 22(2), 216-224.
  • Gorzin, F., Namiranian, M., Omid, M., & Bayat, M. (2018). Comparison between artificial neural network and regression analysis methods to predict and estimate the volume of logging trees in the kheyroud forest of Noshahr. Forest and Wood Products, 71(2): 117-126.
  • Bhering, L.L., Cruz, C.D., Peixoto, L.D, Rosado, A.M., Laviola, B.G., & Nascimento, M. (2015). Application of neural networks to predict volume in eucalyptus. Crop Breeding and Applied Biotechnology, 15, 31-125.
  • Bayat, M., Namiranian, M., Omid, M., Rashidi, A., & Babaei, S. (2016). Applicability of artificial neural network for estimating the forest growing stock. Iranian Journal of Forest and Poplar Research, 24(2),14-226.
  • Lewis, C. (1982). International and Business Forecasting Methods, London, Boston, Butterworths Sceintific Publishing.

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