Prediction of the Slope Solute Loss Based on BP Neural Network

The existence of soil macropores is a common phenomenon.Due to the existence of soil macropores,the amount of solute loss carried by water is deeply modified,which affects watershed hydrologic response.In this study,a new improved BP(Back Propagation)neural network method,using Levenberg–Marquand training algorithm,was used to analyze the solute loss on slopes taking into account the soil macropores.The rainfall intensity,duration,the slope,the characteristic scale of macropores and the adsorption coefficient of ions,are used as the variables of network input layer.The network middle layer is used as hidden layer,the number of hidden nodes is five,and a tangent transfer function is used as its neurons transfer function.The cumulative solute loss on the slope is used as the variable of network output layer.A linear transfer function is used as its neurons transfer function.Artificial rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this model.The error analysis and the performance comparison between the proposed method and traditional gradient descent method are done.The results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent method.In addition,using the experimental data,the influence of soil macropores on slope solute loss has been further confirmed before the simulation.

Modelling of the Slope Solute Loss Based on Fuzzy Neural Network Model

In regards to soil macropores,the solute loss carried by overland flow is a very complex process.In this study,a fuzzy neural network(FNN)model was used to analyze the solute loss on slopes,taking into account the soil macropores.An artificial rainfall simulation experiment was conducted in indoor experimental tanks,and the verification of the model was based on the results.The characteristic scale of the macropores,the rainfall intensity and duration,the slope and the adsorption coefficient of ions,were chosen as the input variables to the Sugeno FNN model.The cumulative solute loss quantity on the slope was adopted as the output variable of the Sugeno FNN model.There were three membership functions,and the type of membership function was gbellmf(generalized bell membership function).The hybrid learning algorithm,which combines the back propagation algorithm with a least square method,was applied to train and optimize the network parameters,and the optimal network parameters were determined.The simulation results showed that the simulated values were consistent with the measured values.

Microstructure evolution of coke under CO2 and H2O atmospheres

The microstructure of coke has an important influence on its thermal properties.The solution loss reactions of coke in CO2 and H2O atmospheres were investigated by in situ observation.The results showed that the isotropic components had a more vigorous reaction than the anisotropic components,and the solution loss reaction of the fine-grained mosaic structure was faster than that of the coarse-grained mosaic structure under the CO2 and H2O atmospheres.The coarse-grained mosaic structure and the flowing structure had a relatively higher anti-erosion ability in the CO2 atmosphere than in the H2O atmosphere,and there was no distinct difference in the solution loss of the isotropic structure under the CO2 and H2O atmospheres.The electron probe microanalysis showed that the Al-Si-Fe compounds in the carbon matrix had positive influence on the solution loss reaction of the anisotropic structure.The iron compounds were able to destroy the pore walls of coke and accelerate the solution loss rate of coke.