Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor
As a key area of China's construction in recent years, the Sino Nepal traffic corridor has complex geological conditions and frequent geological disasters, especially landslides are the most serious. Through the field survey and remote sensing interpretation along G216 highway, we obtained the data of 169 disaster points. Using the MaxEnt model and 8 evaluation factor layers, we predicted the distribution of landslide susceptibility in the study area. We divided the results into five categories: extremely low, low, medium, high and extremely high prone areas, and their proportions are 11.48%, 41.28%, 25.21%, 10.87%, 11.16%. The probability of landslide occurrence is higher near the road and lower the farther away from the road. In addition, we used the jackknife to test the contribution of evaluation factors to the prediction results, and determine the dominant factors. The study provides a high-accuracy analysis model for the prediction of geological disasters in the China Nepal border highway area, and also provides effective support for highway slope disaster prevention in Qinghai Tibet region.
Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor
As a key area of China's construction in recent years, the Sino Nepal traffic corridor has complex geological conditions and frequent geological disasters, especially landslides are the most serious. Through the field survey and remote sensing interpretation along G216 highway, we obtained the data of 169 disaster points. Using the MaxEnt model and 8 evaluation factor layers, we predicted the distribution of landslide susceptibility in the study area. We divided the results into five categories: extremely low, low, medium, high and extremely high prone areas, and their proportions are 11.48%, 41.28%, 25.21%, 10.87%, 11.16%. The probability of landslide occurrence is higher near the road and lower the farther away from the road. In addition, we used the jackknife to test the contribution of evaluation factors to the prediction results, and determine the dominant factors. The study provides a high-accuracy analysis model for the prediction of geological disasters in the China Nepal border highway area, and also provides effective support for highway slope disaster prevention in Qinghai Tibet region.
Landslide susceptibility assessment based on MaxEnt model of along Sino-Nepal traffic corridor
Yang WAN (Autor:in) / Jie GUO (Autor:in) / Fengshan MA (Autor:in) / Jia LIU (Autor:in) / Yewei SONG (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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