Spatial prediction of highway slope disasters based on convolution neural networks

Abstract

In order to clarify the spatial differentiations of highway slope disasters (HSDs) in Boshan District, spatial prediction was carried out based on ECG-CNN with the support of GIS. Spatial prediction factors of HSDs were selected, the stabilities of the 147 highway slopes in Boshan District were determined. The spatial prediction model of HSDs was established by ECG-CNN, and the spatial susceptibility map of HSDs in Boshan District was drawn. Influences of the prediction factor combinations and the drill sample & verification sample combinations on the prediction success rates were verified. The results showed that low susceptible areas, medium susceptible areas and high susceptible areas account for 56.92%, 28.46% and 14.62% of the total areas of Boshan District respectively. Some sections of Binlai Expressway, G205, G309, S210 and S307 pass through high susceptible areas. The surface cutting depth has a small impact on the prediction success rate, while the elevation and gradient have great impacts on the prediction success rate. When the drill samples are small, network drill's maturity has a great impact on the prediction success rate, while when there are many drill samples, the model's logical structure itself has a large impact on the prediction success rate.

The information propagation mechanism of individual heterogeneous adoption behavior under the heterogeneous network

“…It can be applied to analyze financial behaviors [3,4], social information diffusion [5], and emotional contagion [6]. Furthermore, it can be utilized for disaster prediction [7] and risk mitigation [8]. Scholars have explored the mechanisms of social communication through both theoretical analysis and extensive experimental validation.…”

Fuzzy Comprehensive Evaluation Method for Evaluating Stability of Loess Slopes

“…The numerical analysis methods mainly include the finite element method [22], the boundary element method [23], the finite difference method [24], the discrete element method [25], and various machine learning methods [26][27][28]. Uncertainty analysis methods mainly include probability analysis method [29], information quantity model method [30], reliability analysis method [31], gray system evaluation method [32], fuzzy comprehensive evaluation method [33], other uncertainty analysis methods, and composite methods.…”