A Modified Radial Basis Function Method for Predicting Debris Flow Mean Velocity

Abstract

Abstract. This study focused on a model for predicting debris flow mean velocity. A total of 50 debris flow events were investigated in the Jiangjia gully. A modified radial basis function (MRBF) neural network was developed for predicting the debris flow mean velocity in the Jiangjia gully. A threedimensional total error surface was used for establishing the predicting model. A back propagation (BP) neural network and the modified Manning formula (MMF) were used as benchmarks. Finally, the sensitivity degrees of five variables that influence debris flow velocity were analyzed. The results show that the mean error and the relative mean error of the 10 testing samples were only 0.31 m/s and 5.92%, respectively. This proves that the MRBF method performed very well in predicting debris flow mean velocity. Gradient of channel and unstable layer thickness have a greater impact on debris flow mean velocity than the other three influencing variables. This proves that the proposed MRBF neural network is reliable in predicting debris flow mean velocity.

A Mechanistic Model of Mass Transfer in the Extraction of Bioactive Compounds from Intact Sorghum Pericarp

“…In the research that was conducted by Pinela et al [15] and Pace et al [30], the mathematical model was also evaluated in terms of its response to various parameters and variables. In this study, a sensitivity test was conducted by decreasing or increasing the value of each variable by one and comparing the solutions [43]. Figures 5 and 6 show the effects of the changes in some parameter values on the profile of the phytochemical P's concentration in the extract.…”