The Use of Artificial Neural Network to Evaluate the Effects of Human and Physiographic Factors on Forest Stock Volume

Increase in human factors coupled with physiographic factors will impact stock volume in forest ecosystems. the scale of this process and critical information in forestry management provide an incentive for the development of model to predict the forest stock volume. in this paper, we use data derived from siahrood, guilan province, iran using field inventory by cluster sampling in a network (1 × 1 km) with 90 clusters and 900 circular plots (1000 m^2). to evaluate modeling approaches for stock volume responses to changing condition. the relationship between the standing volume and human factors and each physiographic factor were examined using pearson and the artificial neural network method. based on field observations it was observed that different stock volume exhibit in specific physiographic response to population density, livestock density, distance from village, aspect, slope and elevation. results show that multilayer neural networks with 12 nodes can predict the forest stock volume with the lowest rmse (48.76 m^3). in addition, the artificial neural network designed for the buffer of three populations with 85.5% accuracy was selected as the best model to predict the volume based on the mentioned components. the results suggest ann is an effective approach to predict exact forest stock volume and human factors in certain topography conditions and provides useful information for the acceptable amount of standing inventory using the present human population in future experiment.