Research On Construction Of Landslide Spatial Prediction Model And Parameter Optimization

Landslide is one of the common geological disasters and has become a global problem that threatens the safety of human life and property.The inducing factors of landslides are various,and have the characteristics of suddenness and randomness,which often bring serious losses to people.Therefore,it is of great significance to carry out regional landslide spatial prediction research to reduce the losses caused by landslide disasters,and can also provide a scientific basis for regional landslide prevention work.This paper systematically studies the construction of landslide spatial prediction model and parameter optimization method based on machine learning.This paper systematically studies the construction of landslide spatial prediction model and the method of parameter optimization,taking the landslide in Chenggu County,Shaanxi Province as an example.The research results are as follows:(1)According to the characteristics of landslide and geological environment in the study area,16 factors were selected:elevation,slope,slope aspect,plane curvature,profile curvature,sediment transport index(STI),stream power index(SPI),topographical wetness index(TWI),road buffer,river buffer,fault buffer,rainfall,normalized difference vegetation index(NDVI),soil,lithology and land use as Influencing factors of landslide spatial prediction research in the study area;The multicollinearity test was carried out on 16 landslide influencing factors,and the results showed that the influencing factors were independent of each other;the correlation attribute evaluation(CAE)method was used to evaluate the contribution of 16 landslide influence factors;the correlation between landslides and each grade of impact factors was analyzed by the coefficient of certainty factor(CF)method,that 16 factors are scientific and reasonable to evaluate the landslide susceptibility in this area.(2)In this paper,the grid search method is used for functional tree(FT),alternating decision tree(ADT),logistic model tree(LMT),reduced-error pruning tree(REPT),Baggingfunction tree(Bag-FT),Bagging-reduced-error pruning tree(Bag-REPT),Bagging-logistic model tree(Bag-LMT)and Bagging-alternating decision tree(Bag-ADT)for parameter optimization,and the area under the receiver operating characteristic curve(AUC)of the model before and after optimization was compared.The results show that the AUC values of the eight models after parameter optimization have increased to varying degrees,indicating that the predictive ability has been enhanced to varying degrees.(3)Calculated statistical models(CF),machine learning single models(REPT,FT,LMT,ADT),machine learning ensemble models(Bag-REPT,Bag-FT,Bag-LMT,Bag-ADT)and optimization models(REPT,FT),LMT,ADT,Bag-REPT,Bag-FT,Bag-LMT,Bag-ADT)of Chenggu County landslide susceptibility prediction values.And in the GIS software,the natural discontinuity method is used to divide it into five susceptibility zones:extremely high,high,medium,low,and extremely low.After the landslide point density and frequency ratio test,it is found that the susceptibility partition results of the optimized models(Bag-REPT,Bag-FT,BagLMT,Bag-ADT)are more reasonable than other models.(4)The Receiver Operating Characteristic(ROC)curve and its statistical parameters were used to compare the accuracy and generalization ability of the model.Comparing the prediction accuracy of each model,it is found that the model after hyperparameter optimization is more suitable for the spatial prediction of landslide disasters in Chenggu County.The landslide susceptibility map can provide a reference for land planning and utilization,disaster prevention and mitigation in the study area.