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 m2). 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 (48.76m3). 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.Keywords: stock volume, human factors, physiographic factors, neural network Corresponding autho

ФАКТОРНЕ ДОСЛІДЖЕННЯ СОБІВАРТОСТІ МІЖНАРОДНИХ АВТОМОБІЛЬНИХ ПЕРЕВЕЗЕНЬ ПАРТІОННИХ ВАНТАЖІВ

One of the important tasks to be solved by carriers and freight forwarders in the organization of international road transportation of party cargoes is the choice of effective methods of cargo transportation. For this purpose it is expedient to estimate each method on criteria of efficiency of the international road transportations of party cargoes (time, financial, etc.). A very important financial criterion is the prime cost of 1 km of the vehicle mileage during the international road transportation of party cargoes. The influence of factors on it, such as: mileage of vehicles, operating costs for international road transportation of party goods, the volume of the consignment of goods transported. Factor analysis was performed by different methods: multiple regression, nonlinear multiple regression and neural networks. According to the results of research, we can conclude that the smallest balances in the prediction of neural networks, because they are self-improving and give a more accurate result.One of the important tasks to be solved by carriers and freight forwarders in the organization of international road transportation of party cargoes is the choice of effective methods of cargo transportation. For this purpose it is expedient to estimate each method on criteria of efficiency of the international road transportations of party cargoes (time, financial, etc.). A very important financial criterion is the prime cost of 1 km of the vehicle mileage during the international road transportation of party cargoes. The influence of factors on it, such as: mileage of vehicles, operating costs for international road transportation of party goods, the volume of the consignment of goods transported. Factor analysis was performed by different methods: multiple regression, nonlinear multiple regression and neural networks. According to the results of research, we can conclude that the smallest balances in the prediction of neural networks, because they are self-improving and give a more accurate result