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

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.