Risk Assessment Of Geological Hazards In Southern Shaanxi Based On RS And Machine Learning

In southern Shaanxi,where the geological conditions are complex and faults are densely distributed,geological disasters such as collapse,debris flow and landslide are widely distributed,threatening the safety of life and property of residents.Based on machine learning and remote sensing technology,this paper takes the southern Shaanxi region as the research area to conduct risk assessment of geological disasters,and the results can provide a reference for disaster warning and prevention and control.The main contents and conclusions of this paper are as follows:1.Collect climate,geography and geological disaster data in southern Shaanxi,and study and summarize the distribution and development characteristics of regional geological disasters.It is found that the geological disasters in the study area are mainly concentrated in the middle and low mountainous areas of Qinba area,and the distribution of disasters is highly correlated with the distance of faults and lithologic components.2.From the perspective of environmental conditions and external inducing factors,16 indicators such as topography,fault buffer zone and rainfall were selected to establish an index system.The GIS technology is used to extract the data of the index layer,and the correlation analysis and collinearity analysis are carried out to screen the indicators by calculating the index information gain ratio,so as to remove the low-quality indicators and solve the subjectivity problem in the selection process of indicators.Finally,the correlation between each index and geological disaster is analyzed and studied.3.Conduct disaster susceptibility assessment in southern Shaanxi based on different evaluation units and models,which improves the credibility of the research.Based on the analysis of the evaluation results,it is found that the disasters are mainly concentrated around roads,loess and soft rock areas,indicating that road construction and lithology are the leading factors of disasters.4.The vulnerability assessment was carried out from the perspectives of population density distribution and economic value of disaster bearing bodies.The economic value of the disaster bearing body was estimated according to the type of disaster bearing body and the current market price,and the population distribution was estimated using the population data of the National Bureau of Statistics and the nighttime light image.Then,the value and population density of the raster evaluation unit were calculated to complete the vulnerability evaluation,and the vulnerability zoning map was drawn.The results showed that the road network in Ankang and Hanzhong was dense and the population of river valley was concentrated.5.The risk assessment model is built based on the post-disaster loss of the disaster bearing body,the disaster resistance ability,the susceptibility and vulnerability of geological disasters.A semi-quantitative method is used to assign weights to the post-disaster loss and anti-disaster capability of different disaster bearing bodies.The data of vulnerability and vulnerability are brought into the model to complete the calculation,and the zoning map is drawn to complete the geological disaster risk assessment in southern Shaanxi.Using Qt development platform based on C++ and Python programming languages to make geological hazard risk assessment module.This module integrates the vulnerability evaluation process and risk evaluation process of this paper,which can easily and quickly apply the risk evaluation model in practice.