Estimation of Land Soil Erosion Using Neural Network Model


The land surface erosion is controlled by multifarious of different parameters, such as slope, soil physical properties (texture, structure, permeability, etc.), rainfall, runoff, and crop cover. However, it is impossible to develop precise simplest mathematical model that can predict the values of land surface soil erosion due to the behavior of controlled parameters. This paper presents the Neural Networks Model for assessing land surface soil erosion as amass per unit area per unit of time. The model derives from the analysis data obtained from available literature and was formulated as linear regression model and back propagation algorithm neural model. Both models were built by correlating firstly five watersheds variables with land surface erosion and secondly ten watershed variables with land surface erosion. The coefficients for independent variables were highly significant for both models. The case of correlating 10- watershed variables with land surface erosion gives R=0.978 & 0.976 for both models which is higher than that for 5- watershed variables. The mean absolute relative error (MARE%) is another procedure that used in order to evaluate the accuracy of the model and The average error % is 0.025 for (5) variables and 0.0064 for (10) variables. Both the supporting practices (P) and the slope length and slope steepness (LS) coefficients have a marked effect on the amount of land surface erosion in the case of 5- watershed variables. The amount of land surface erosion show a high level of sensitivity to the content of fine sand% in soil (FS) watershed variables on The amount of land surface soil erosion.