جستجوی مقالات مرتبط با کلیدواژه « MLP » در نشریات گروه « جنگلداری »

In this study, we investigated the effects of forest operation on runoff and sediment using small-scale plots. The runoff and sediment samples were collected from 36 sample plots with dimensions of one by two meters in different areas of the operation. We used the Multi-Layer Perceptron (MLP) for modeling, with 65% of the data for training, 10% for validation, and 25% for testing. We evaluated the accuracy of the model using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE), and compared our results with optimized models obtained through trial and error. We collected information and produced runoff and sediment maps using GIS. Our results showed that the most important factors affecting runoff production were soil bulk density, rainfall intensity, slope, rainfall values, percentage of grass cover, and canopy cover percentage. For sediment, the most important factors were rainfall intensity, soil bulk density, slope percentage, and surface cover percentage. The MSE and R values for runoff modeling were 0.009 and 0.9 in the training stage and 0.01 and 0.82 in the test stage, respectively. For sediment modeling, the MSE and R values were 0.01 and 0.86 in the training stage and 4.3 and 0.8 in the test stage, respectively. Our results showed that neural networks have high capability in modeling runoff and sediment in forest lands. We also conducted an overlap analysis to measure the accuracy, precision, and efficiency of the results and methods presented in our study. Therefore, the proposed model can be used to combine ANN and GIS in the simulation and modeling of runoff and sediment in forest areas."

In this research, the application of Artificial Neural Network or MLP method in the process Assignment of relevé-groups/ plant communities allocation was evaluated using Buxus hyrcana forests database. For this purpose, firstly, the ecological and sociological groups of B. hyrcana were determined using TWINSPAN and Braun-Blanquet method, respectively. The results of both numerical and expert based classification dendrogram of the B. hyrcana communities, which included seven levels of classification as primary groups/plant communities, were introduced to MLP. Then, with assignments in three sets of training (70%), test (15%) and validation (15%), the MLP classification was performed on each level of the two dendrogram. The results showed that by increasing the level of classification, the degree of adaptation of the MLP result to primary results of TWINSPAN (99% to 60%) and Braun-Blanquet (98% to 68%) from the cutoff level of 1 to 7. Results of sensitivity and kappa cross tab coefficients, except in 7 cut level, imply that the quality of MLP groups based on TWINSPAN primary ecological group is upper than primary Braun-Blanquet groups.Appropriate adaptation of the MLP results in Buxus hyrcana plant communities classification with the TWINSPAN and Braun-Blanquet ecological/syntaxa groups at the fifth cut level of both dendrograms indicate that application of MLP aim at a reliable results in plant community classification. So our result reiterate that MLP could be introduced as a suitable method in the assignment of releves to plant communities.

Different factors influence on the properties of wood composite panels. Evaluating all of these factors not only increases waste of time and energy but also decreases accuracy in estimation of influence value of selected factors in manufacturing panels in order to obtain optimum point of different properties of wood composite panels. Hence, application of a new statistical method is necessary for determination of model estimating production´s optimum point. This study was aimed to evaluate the artificial neural networks performance to model the physical properties of the particleboard made of canola stalks particles. The physical properties of the particleboard were modeled and estimated using different weight ratios of melamine formaldehyde to urea formaldehyde, canola stalks to poplar particles and mat moisture content through MLP, RBFN and ANFIS artificial neural networks. The results showed that MLP neural network has better performance than RBFN and ANFIS neural networks to estimate the physical properties of the particleboard. The results also showed that the artificial intelligence models have a proper precision and ability to predict the particleboard's physical properties. The results of the sensitivity analysis also showed that for estimating and , the most important parameter was mat moisture content with a positive effect on the modeling, and melamine formaldehyde to urea formaldehyde ratio was also the most effective parameter for estimating and .