Susceptibility mapping and zoning of highway landslide disasters in China

Abstract

Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

Spatial prediction of highway slope disasters based on convolution neural networks

“…Due to the different dimensions of the prediction factors and the large differences in the data ranges, data normalization proposed by Yin et al [2,6] was conducted before disaster spatial prediction. The details will not be repeated here.…”

“…Highway slope disasters (HSDs) include collapse, landslide, debris flow and slope erosion that often occur on natural or artificial slopes along highways to damage subgrade, pavement, bridges, tunnels and other structures [1]. The prevention and control of HSDs can improve the disaster resistance of highway network and accelerate the construction of "traffic power" [2]. Spatial prediction is the prerequisite for disaster monitoring and early warning based on the fusion of diverse and heterogeneous geographic, geological and hydrological information, which is of great significance to reduce economic losses and casualties [3][4][5][6].…”

Research on the application of a cement and soil aggregate for the ecological restoration of vegetation in artificial soil

“…Cut slopes pose more potential hazards in areas with mountains and hills. Landslides have caused disastrous traffic accidents; in 2007, 24,993 geomorphological accidents occurred in China, of which 15,478 were landslides ( Gao & Sang, 2017 ) and 10% (1,543) of those were highway landslide disasters ( Yin et al, 2020 ). Nearly 1,100 fatalities and $5–10 billion USD worth of damage have been caused by landslide disasters in China annually since 2000 ( Hong et al, 2017 ).…”

Assessment of Landslide susceptibility and risk to road network in Mt Elgon, Uganda

“…As indicated by Hearn et al (2019) it is important that the stability of the road reserve and its associated engineering assets are maintained as a priority for the Uganda National Road Authority as well as District Local Governments, and that this necessitates consideration of earthworks slopes as well as the wider landscape in which the road is constructed. Susceptibility mapping and zoning can reveal the spatial differentiations of HLDs (Yin et al, 2020) and this combined with use of Algorithm can reveal alternative safer and less risky routes for road construction (Kadi et al, 2019).…”