Tree volume is a characteristic used in many cases, such as determining fertility, habitat quality, growth size, allowable harvesting, and the principles of forest trade. It is imperative to develop methods that predict forest stand volume to obtain this extensive information quickly and cost-effectively. This study used supervised self-organising map (SSOM), multi-layer perceptron (MLP), and radial basis function (RBF) neural networks to predict forest stand volume based on physiography, topography, soil, and human factors. A sensitivity analysis method called the importance of prediction was used to determine how input variables influenced network output. First, the map of homogeneous units was prepared with ArcMap (Version 10.3.1, 2015) by combining digital layers to measure the tree's volume per hectare. Then, separate tree species in different diameter classes were measured in a circular grid of 200 m x 150 m, 0.1 ha of coverage, 3.3% sampling intensity, and a diameter at breast height (DBH) greater than 7.5 cm using systematic sampling on a homogeneous unit map in a regular random method. The neural network modelling results showed that SSOM, MLP, and RBF predicted forest stand volume most accurately according to physiography, topography, soil, and human factors. Furthermore, the sensitivity analysis results found that altitude above sea level, soil depth, and slope are the most influential input variables. In contrast, soil texture variables are the least effective at predicting forest stand volume.
