Modeling the commercial volume of trees in mixed beech stands of Hyrcanian forests through artificial neural network

Predicting the volume of standing trees precisely is the basis of growth rate, amount of allowable harvesting, aboveground biomass carbon sequestration, and the foundation of optimal management according to the sustainable development. New technology of artificial intelligence including artificial neural network (ANN) was applied for modelling and predicting the commercial volume of measured trees in district 3 of Glandroud forests. The data of renewed volume table was acquired from bureau of natural resources and watershed management of Mazandaran province, Nowshahr. Diameter and total height of 150 fallen trees were used as inputs to develop the stage-wise modeling by feed forward back-propagation (FFBP). Two non-linear functions, Logsig and Tansig, were applied as transfer functions. Each function with the same topology showed the different outputs having different accuracies. After initial weighting and training algorithm, transfer functions of neurons had different rotation for decreasing the errors. After each trial, which led to various topology functions, the result showed that the model including diameter and total height with transfer function of Logsig, topology of one hidden layer and fifteen neurons, was the best model to predict the volume of trees in this study. The mentioned model provided the considerable accuracy with the highest coefficient of determination (R2 = 0.99), the least mean squared error of test (MSE) and the least average deviation (AD = 0.158).