جستجوی مقالات مرتبط با کلیدواژه "layered perceptron" در نشریات گروه "اکولوژی"

Over the past two decades, degradation damage has increased in natural ecosystems. This research focuses solely on the degradation of soil and vegetation cover in Sorkheh Eshpar National Park. For this purpose, some physical properties of the soil (specific gravity and true porosity, moisture content, a percentage of pebbles, acidity and conductivity and soil texture) were measured. Also, the diversity and density of the crop in two safe zones and other uses were calculated. Soil sampling was carried out in the form of strip transects at the beginning and the end of each joint unit and excavation of four soil profiles at a depth of 5-5 cm. The crown cover was taken with 2 × 2 squares throughout each transect and 30 plots per unit. A total of 600 plots and 480 soil profiles were taken from the study area. Shannonchr('39')s biodiversity index was selected as an output variable and soil characteristics, ecological factors, and human factors as inputs of the model. Normality of data was confirmed by Shapiro-Wilk test. SPSS software was used for soil degradation regression and vegetation cover. The combined, removable, and retrograde method was chosen with a value of = 422.0. With the MATLAB software, the artificial neural network was created with a multi-layered perceptron, with a secret layer, and 18 neurons, and a sigmoid tangent function, and a value of R = 0.5065. Considering the amount of R in the artificial neural network model, this model was selected as the optimum model for estimating and predicting soil degradation and vegetation cover in Sorkho-e Hesar National Park.