Comparison of the efficiency of artificial neural network and regression in predicting the skidding time of steel-tracked skidder and agriculture tractor

Having accurate information about the efficiency of skidding machines in order to reduce transportation costs inforest engineering studies using modern statistical models is very valuable. In this study, the prediction of the skiddingtime in steel tracked skidder and agriculture tractor was performed using an artificial neural network and multiple linearregression model and then the efficiency of the models was compared. The variables of skidding distance, slope, andvolume in each skidding cycle as independent variables (input variable) and time of each skidding cycle as thedependent variables (response variable) were entered into the model. The results showed the prediction in skidding timeof steel tracked skidder, the explanation coefficient of the MLP neural network and regression model were 0.78 and0.55, respectively and the error rate of models was 0.19 and 0.42, respectively. Also, in the agricultural tractor system,the explanation coefficient of MLP neural network and regression model were 0.70 and 0.62, respectively, and the errorrate of models was 0.18 and 0.28, respectively. Therefore, in both skidding systems, MLP neural network is moreefficient in predicting skidding time than the multiple linear regression model. Sensitivity analysis of the artificialneural network and regression showed that the skidding distance variable in the steel tracked skidder chain wheel andthe skidding path slope variable in the agricultural tractor are the most important.