Neural Networks for Prediction PDF
Soil erosion is the one of most important environment problem in the world nowadays, which is dominated by water erosion caused by water, and water and soil erosion on slope is the initiatory and desicive stage of water erosion. Reseach on the mechanics mechanism of water and soil erosion on slope and compulating oerland flow and sediment yield and analying the effective factors have practical significance to prevent soil erosion caused by water.In this study, based on observational data in eight runoff plots along Suining-Chongqing railway in Chuanzhong hilly area and mechanical analysis, loss regularity was concluded and reliable data was screened. And using BP neural network and artificial neural network model to modeling predict the five-factor slope soil erosion.On the basis of research on splash erosion and runoff erosion of erosion on slope, the mechanism was summarized, the mechanism of its erosion mechanics was deduced, and the expression of the rainfall erosivity was derived, based on which, mechanical model of soil erosion on slope in macro-scale was established.Studies suggest that the separation soil particles on slope caused by the slope runoff erosion is the result of combined action of dynamic factor and resistance factor, meaning that the runoff shear stress is greater than or equal to the critical drag force on slope, which is the essential requirements and prerequisite of the separation of the soil particles on slope.Establishing hydraulic erosion runoff and sediment dynamics model, with rail project as a carrier analyzing and numerical simulating the factors that affecting runoff and dumping pile of slope erosion runoff and sediment dynamics model, to produce numerical simulation of between the flow process, the depth of the process and a single sediment load on slope and influence factors, and expect to provide a scientific basis for soil conservation work for residue field inthis type of development and construction projects.The main research results are as follows:1.Slope of soil particles by slope runoff erosion separation is the result of dynamic factors and resistance factors, the Runoff shear stress greater than or equal to the slope critical drag force.It is necessary and a prerequisite of the slope the soil particles separated.2.Based on Kinematic wave theory, we can deduced the theoretical relationship of unit discharge and overland flow velocity, and given the expression. The resulting formula be seen that the main impact and constraints of a single-width and flow of the surface conditions are Manning roughness coefficient slope depth and slope, slope length and gradient, etc.;Assuming that constant along the hillslope soil properties and simplification of a number of constant, we can get the simple slope soil erosion expression formula.3.Quadrature on soil erosion rates along the slope length direction, we can draw a simple formula of Soil Erosion on Hillslopes obtained slope sediment yield of the main factors for slope gradient a, slope length L, unit processes a single-width flow runoff rate qw, Manning roughness coefficient n (n is the comprehensive reflection of the wall flow blocking effect of roughness coefficient) and surface friction coefficient f and sediment diameter d. It is reflects the relationship between the existence of the critical slope and only two unknowns slope sediment yield and the factors by Matlab numerical simulation.When the analysis and numerical simulation of complex slope soil erosion formula is over, it derive complex hillslope erosion factor influencing primary and secondary role and the role of the law.4.When the normalized values range from 0.05 to 0.95, the results of predict will be best.For a single – hidden – layer BP network, by far the number of the hidden layer neurons can not be determined by a fixed fonnula.We find that when the number of the hidden layer neurons is within ION the simulating results is better or the best.Forecast period, when the number of training samples are same, the larger of the coefficient of determination of the input factors to predict the effect the better. When the number of training samples are more than before, the differences in the coefficient of determination did not affect the forecast results. Generally speaking, the best of the predicted results in the large number of samples and the coefficient of determination. Therefore, when the number of samples is low, the BP neural network modeling prediction by correlation analysis to filter the data.5.For the slope soil erosion prediction of the central Sichuan Hilly Region, we can get satisfactory results by establishing artificial intelligencebased models such as BP neural network in Predicting soil erosion as long as the training samples are enough and the input factors are appropriately selected.
Source: Agricultural Science Paper
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