Forest fuel loading estimates based on a back propagation neutral network

Forest fuel loading is an import factor related to the fire behavior;therefore,forest fuel loading estimates are very important for forest fire management.This study used data collected in 32 Larix Gmelinii forest sample locations in the Daxing'anling region to build a multivariable regression equation and a back propagation neural network model to estimate forest fuel loadings from stand factors,such as tree age,crown cover,average tree height,and diameter at breast height.The back propagation neutral network training and simulation used for models in MATLAB with the multivariable regression equation developed using SPSS.The fitting precision of the back propagation neutral network model was 99.9% with an extrapolation precision of 65.51%.The fitting precision of the multivariable regression equation was 68.29% with an extrapolation precision of 62.1%.Thus,the back propagation neutral network model for estimating forest fuel loading from the stand factors is more accurate than the multivariable regression equation.The extrapolation accuracy of both the BP neutral network model and the multivariable regression model are less than 70% due to the small number of samples.